This PDF file contains the front matter associated with SPIE Proceedings Volume 8293, including the Title Page, Copyright information, Table of Contents, and the Conference Committee listing.

The I3A Camera Phone Image Quality (CPIQ) initiative aims to provide a consumer-oriented overall image quality metric for mobile phone cameras. In order to achieve this goal, a set of subjectively correlated image quality metrics has been developed. This paper describes the development of a specific group within this set of metrics, the spatial metrics. Contained in this group are the edge acutance, visual noise and texture acutance metrics. A common feature is that they are all dependent on the spatial content of the specific scene being analyzed. Therefore, the measurement results of the metrics are weighted by a contrast sensitivity function (CSF) and, thus, the conditions under which a particular image is viewed must be specified. This leads to the establishment of a common framework consisting of three components shared by all spatial metrics. First, the RGB image is transformed to a color opponent space, separating the luminance channel from two chrominance channels. Second, associated with this color space are three contrast sensitivity functions for each individual opponent channel. Finally, the specific viewing conditions, comprising both digital displays as well as printouts, are supported through two distinct MTFs.

The I3A Camera Phone Image Quality (CPIQ) visual noise metric described is a core image quality attribute of the wider I3A CPIQ consumer orientated, camera image quality score. This paper describes the selection of a suitable noise metric, the adaptation of the chosen ISO 15739 visual noise protocol for the challenges posed by cell phone cameras and the mapping of the adapted protocol to subjective image quality loss using a published noise study. Via a simple study, visual noise metrics are shown to discriminate between different noise frequency shapes. The optical non-uniformities prevalent in cell phone cameras and higher noise levels pose significant challenges to the ISO 15739 visual noise protocol. The non-uniformities are addressed using a frequency based high pass filter. Secondly, the data clipping at high noise levels is avoided using a Johnson and Fairchild frequency based Luminance contrast sensitivity function (CSF). The final result is a visually based noise metric calibrated in Quality Loss Just Noticeable Differences (JND) using Aptina Imaging's subjectively calibrated image set.

This paper presents a method for evaluating the performance of camcorders in terms of texture preservation, taking into account the contrast sensitivity function of human visual system. A quality metric called texture preservation ratio (TPR) is the outcome of the method. It quantifies to what extent texture structures are preserved in a video recorded by a camcorder. In our experiments, we used the dead leaves chart to simulate a scene with textures of different scales. The dead leaves chart is known as a good target for testing purposes because it is invariant to scaling, translation, rotation, and contrast (exposure) adjustment. Experimental results have shown the following observations on five tested camcorders from three different vendors: 1) the TPR value decreases monotonically with respect to the motion speed; 2) the TPR value increases monotonically with respect to the lossy compression bitrates. Thereby, our study has confirmed TPR as a useful indicator for measuring a camcorder's performance in terms of preserving textures.

The pixel race in the digital camera industry and for mobile phone imaging modules have made noise reduction a significant part in the signal processing. Depending on the used algorithms and the underlying amount of noise that has to be removed, noise reduction leads to a loss of low contrast fine details, also known as texture loss. The description of these effects became an important part of the objective image quality evaluation in the last years, as the established methods for noise and resolution measurement fail to do so. Different methods have been developed and presented, but could not fully satisfy the requested stability and correlation with subjective tests. In our paper, we present our experience with the current approaches for texture loss measurement. We have found a critical issue within these methods: the used targets are neutral in color. We could show that the test-lab results do not match the real live experience with the cameras under test. We present an approach using a colored target and our experience with this method.

In a preliminary report, we showed the impact of the integrating cavity effect for a typical document scanner with optical ray tracing. The effect was demonstrated by examining the illumination profile after accounting for multiple reflections from the document surface, the contact platen glass surfaces and all reflectors used in the illumination assembly. We identified that the platen glass can contribute just as much as the illumination assembly to the effect. In the second phase, we built an actual scanner model to verify the ray tracing results and the effect. The verification was accomplished by examining the edge profile differences of the scan images of unique patterns before and after a certain reflection component was removed with two different scan configurations. The experimental results are consistent with the simulation results in general.

The modulation transfer function (MTF) is a fundamental tool for assessing the performance of imaging systems. It has been applied to a range of capture and output devices, including printers and even the media itself. In this paper, we consider the problem of measuring the MTF of image capture devices. We analyze the factors that limit the MTF of a capture device. Then, we examine three different approaches to this task based, respectively, on a slant-edge target, a sinewave target, and a grill pattern. We review the mathematical relationship between the three different methods, and discuss their comparative advantages and disadvantages. Finally, we present experimental results for MTF measurement with a number of different commercially available image capture devices that are specifically designed for capture of 2D reflection or transmission copy. These include camera-based systems, flat-bed scanners, and a drum scanner.

While image stabilization(IS ) has become a default functionality for most digital cameras, there is a lack of automatic IS evaluation scheme. Most publicly known camera IS reviews either require human visual assessment or resort to some generic blur metric. The former is slow and inconsistent, and the latter may not be easily scalable with respect to resolution variation and exposure variation when comparing different cameras. We proposed a histogram based automatic IS evaluation scheme, which employs a white noise pattern as shooting target. It is able to produce accurate and consistent IS benchmarks in a very fast manner.

We address the problem of image quality assessment for natural images, focusing on No Reference (NR) assessment methods for sharpness. The metrics proposed in the literature are based on edge pixel measures that significantly suffer the presence of noise. In this work we present an automatic method that selects edge segments, making it possible to evaluate sharpness on more reliable data. To reduce the noise influence, we also propose a new sharpness metric for natural images.

The structural similarity index (SSIM) is a well-known metric in the field of image quality assessment (IQA). It is a full-reference metric that uses a sliding window to determine local quality/distortion measures between two images (based on a combination of luminance, contrast, and structural measurements from each image's window), and combines the results to obtain a single value indicating the quality of the distorted image relative to its perfect reference image. The metric uses a window of arbitrary size and shape to compute the localized measurements, but no work has been done to determine the effects of different window selections. This paper provides insight into these effects by testing various window modifications against six publicly-available image quality databases. Optimal window results are presented as well as a visual mapping of window parameters vs. performance.

The capture and retention of image detail are important characteristics for system design and subsystem selection. An established imaging performance measure that is well suited to certain sources of detail loss, such as optical focus and motion blur, is the Modulation Transfer Function (MTF). Recently we have seen the development of image quality methods aimed at more adaptive operations, such as noise cleaning and adaptive digital filtering. An example of this is the measure of texture (image detail) loss using sets of overlapping small objects, known as dead leaves targets. In this paper we investigate the application of the above method to image compression. We apply several levels of JPEG and JPEG 2000 compression to digital images that include scene content that is amenable to the texture loss measure. A modified form of the method was used. This allowed direct target compensation without data smoothing. Following a camera simulation, the texture MTF and acutance were computed. The standard deviation of the acutance measure was 0.014 (relative error of 1.63%), found by replicate measurements. Structured similarity index (SSIM) values, used for still and video image quality evaluation, were also computed for the image sets. The acutance and SSI results were similar; however the relationship between the two showed an offset between the JPEG and JPEG 2000 images sets.

No Reference Image Quality Metrics proposed in the literature are generally developed for specific degradations, limiting thus their application. To overcome this limitation, we propose in this study a NR-IQM for ringing and blur distortions based on a neural weighting scheme. For a given image, we first estimate the level of blur and ringing degradations contained in an image using an Artificial Neural Networks (ANN) model. Then, the final index quality is given by combining blur and ringing measures by using the estimated weights through the learning process. The obtained results are promising.

Image quality assessment is indispensable for image-based applications. The approaches towards image quality assessment fall into two main categories: subjective and objective methods. Subjective assessment has been widely used. However, careful subjective assessments are experimentally difficult and lengthy, and the results obtained may vary depending on the test conditions. On the other hand, objective image quality assessment would not only alleviate the difficulties described above but would also help to expand the application field. Therefore, several works have been developed for quantifying the distortion presented on a image achieving goodness of fit between subjective and objective scores up to 92%. Nevertheless, current methodologies are designed assuming that the nature of the distortion is known. Generally, this is a limiting assumption for practical applications, since in a majority of cases the distortions in the image are unknown. Therefore, we believe that the current methods of image quality assessment should be adapted in order to identify and quantify the distortion of images at the same time. That combination can improve processes such as enhancement, restoration, compression, transmission, among others. We present an approach based on the power of the experimental design and the joint localization of the Gabor filters for studying the influence of the spatial/frequencies on image quality assessment. Therefore, we achieve a correct identification and quantification of the distortion affecting images. This method provides accurate scores and differentiability between distortions.

Picture adjustment is referred to those adjustments that affect the four main subjective perceptual image attributes: Hue, Saturation, Brightness (sometimes called Intensity) and Contrast--HSIC adjustments. The common method used for this type of adjustments in a display processing pipe is based on YC_bC_r color space and a 3x4 color adjustment matrix. Picture adjustments based on this method, however, leads to multiple problems such as adjusting one attribute leads to degradation of other attributes. As an alternative, other color spaces such as HSV can be used to generate more consistent and effective picture adjustments. In this paper, the results of a comparative performance analysis between the two methods based on YC_bC_r and HSV color spaces (for HSIC adjustments) are presented.

In cross-media colour reproduction, it is common goal achieving media-relative reproduction. From the ICC specification, this often accomplished by linearly scaling XYZ data so that the media white of the source data matches that of the destination data. However, in this approach the media black points are not explicitly aligned. To compensate this problem, it is common to apply a black point compensation (BPC) procedure to improve the mapping of the black points. First, three lightness rescaling methods were chosen: linear, sigmoidal and spline. CIECAM02 was also implemented in an approach of a lightness rescaling method; simply, lightness values from results produced by CIECAM02 handle as if reproduced lightness values of an output image. With a chosen image set, above five different methods were implemented. A paired-comparison psychophysical experiment was performed to evaluate performances of the lightness rescaling methods. In most images, the Adobe's BPC, linear and Spline lightness rescaling methods are preferred over the CIECAM02 and sigmoidal lightness rescaling methods. The confidence interval for the single image set is ±0.36. With this confidence interval, it is difficult to conclude the Adobe BPC' method works better, but not significantly so. However, for the overall results, as every single observation is independent to each other, the result was presented with the confidence interval of ±0.0763. Based on the overall result, the Adobe's BPC method performs best.

A project, supported by the Andrew W. Mellon Foundation, evaluating current practices in fine art image reproduction, determining the image quality generally achievable, and establishing a suggested framework for art image interchange was recently completed. (Information regarding the Mellon project and related work may be found at www.artimaging.rit.edu.) To determine the image quality currently being achieved, experimentation was conducted in which a set of objective targets and pieces of artwork in various media were imaged by participating museums and other cultural heritage institutions. Prints and images for display made from the delivered image files at the Rochester Institute of Technology were used as stimuli in psychometric testing in which observers were asked to evaluate the prints as reproductions of the original artwork and as stand alone images. The results indicated that there were limited differences between assessments made with and without the original present for printed reproductions. For displayed images, the differences were more significant with lower contrast images being ranked lower and higher contrast images generally ranked higher when the original was not present. This was true for experiments conducted both in a dimly lit laboratory as well as via the web, indicating that more than viewing conditions were driving this shift.

In this paper we discuss human assessment of the quality of photographic still images, that are degraded in various manners relative to an original, for example due to compression or noise. In particular, we examine and present results from a technique where observers view images on a mobile device, perform pairwise comparisons, identify defects in the images, and interact with the display to indicate the location of the defects. The technique measures the response time and accuracy of the responses. By posing the survey in a form similar to a game, providing performance feedback to the observer, the technique attempts to increase the engagement of the observers, and to avoid exhausting observers, a factor that is often a problem for subjective surveys. The results are compared with the known physical magnitudes of the defects and with results from similar web-based surveys. The strengths and weaknesses of the technique are discussed. Possible extensions of the technique to video quality assessment are also discussed.

In this paper an evaluation of the degree of change in the perceived image sharpness with changes in displayed image size was carried out. This was achieved by collecting data from three psychophysical investigations that used techniques to match the perceived sharpness of displayed images of three different sizes. The paper first describes a method employed to create a series of frequency domain filters for sharpening and blurring. The filters were designed to achieve one just-noticeable-difference (JND) in quality between images viewed from a certain distance and having a certain displayed image size (and thus angle of subtense). During psychophysical experiments, the filtered images were used as a test series for sharpness matching. For the capture of test-images, a digital SLR camera with a quality zoom lens was used for recording natural scenes with varying scene content, under various illumination conditions. For the psychophysical investigation, a total of sixty-four original test-images were selected and resized, using bi-cubic interpolation, to three different image sizes, representing typical displayed sizes. Results showed that the degree of change in sharpness between images of different sizes varied with scene content.

The physical validation of computer-generated images (CGIs) has received a lot of attention from the computer graphics community, as opposed to the assessment of these images' psychovisual quality. The field indeed lacks the proper tools to quantify the perceptual quality of a CGI. This paper engages in the construction of such a metric. A psychovisual experiment was submitted to a representative panel of observers, where participants were asked to score the overall quality and aspects of this quality for several CGIs. An analytical model was fit to the data, giving insight into the relative perceptual importances of these aspects. Accuracy in the simulation of shadows, good contrast and absence of noise were found to have a major impact on the perceived quality, rather than precise anti-aliasing and faithfull color bleeding.

Assessing product-image quality is important in the context of online shopping. A high quality image that conveys more information about a product can boost the buyer's confidence and can get more attention. However, the notion of image quality for product-images is not the same as that in other domains. The perception of quality of product-images depends not only on various photographic quality features but also on various high level features such as clarity of the foreground or goodness of the background etc. In this paper, we define a notion of product-image quality based on various such features. We conduct a crowd-sourced experiment to collect user judgments on thousands of eBay's images. We formulate a multi-class classification problem for modeling image quality by classifying images into good, fair and poor quality based on the guided perceptual notions from the judges. We also conduct experiments with regression using average crowd-sourced human judgments as target. We compute a pseudo-regression score with expected average of predicted classes and also compute a score from the regression technique. We design many experiments with various sampling and voting schemes with crowd-sourced data and construct various experimental image quality models. Most of our models have reasonable accuracies (greater or equal to 70%) on test data set. We observe that our computed image quality score has a high (0.66) rank correlation with average votes from the crowd sourced human judgments.

The most common tasks in subjective image estimation are change detection (a detection task) and image quality estimation (a preference task). We examined how the task influences the gaze behavior when comparing detection and preference tasks. The eye movements of 16 naïve observers were recorded with 8 observers in both tasks. The setting was a flicker paradigm, where the observers see a non-manipulated image, a manipulated version of the image and again the non-manipulated image and estimate the difference they perceived in them. The material was photographic material with different image distortions and contents. To examine the spatial distribution of fixations, we defined the regions of interest using a memory task and calculated information entropy to estimate how concentrated the fixations were on the image plane. The quality task was faster and needed fewer fixations and the first eight fixations were more concentrated on certain image areas than the change detection task. The bottom-up influences of the image also caused more variation to the gaze behavior in the quality estimation task than in the change detection task The results show that the quality estimation is faster and the regions of interest are emphasized more on certain images compared with the change detection task that is a scan task where the whole image is always thoroughly examined. In conclusion, in subjective image estimation studies it is important to think about the task.

Predicting which areas of an image are perceptually salient or attended to has become an essential pre-requisite of many computer vision applications. Because observers are notoriously unreliable in remembering where they look a posteriori, and because asking where they look while observing the image necessarily in uences the results, ground truth about saliency and visual attention has to be obtained by gaze tracking methods. From the early work of Buswell and Yarbus to the most recent forays in computer vision there has been, perhaps unfortunately, little agreement on standardisation of eye tracking protocols for measuring visual attention. As the number of parameters involved in experimental methodology can be large, their individual in uence on the nal results is not well understood. Consequently, the performance of saliency algorithms, when assessed by correlation techniques, varies greatly across the literature. In this paper, we concern ourselves with the problem of image quality. Specically: where people look when judging images. We show that in this case, the performance gap between existing saliency prediction algorithms and experimental results is signicantly larger than otherwise reported. To understand this discrepancy, we rst devise an experimental protocol that is adapted to the task of measuring image quality. In a second step, we compare our experimental parameters with the ones of existing methods and show that a lot of the variability can directly be ascribed to these dierences in experimental methodology and choice of variables. In particular, the choice of a task, e.g., judging image quality vs. free viewing, has a great impact on measured saliency maps, suggesting that even for a mildly cognitive task, ground truth obtained by free viewing does not adapt well. Careful analysis of the prior art also reveals that systematic bias can occur depending on instrumental calibration and the choice of test images. We conclude this work by proposing a set of parameters, tasks and images that can be used to compare the various saliency prediction methods in a manner that is meaningful for image quality assessment.

Image quality is generally estimated under a controlled environment. However, given the increasing number of images being viewed on observers' own displays, it becomes of interest to understand what might affect the perceived image quality in an uncontrolled environment. In the paper, an experiment conducted to learn the impact of the display white point on the perceived image quality by observers is discussed. Based on the experimental results, the perceived image quality by observers was invariant to the changes in the white point setting of the display.

It is of great value to be able to track image quality of a printing system and detect changes before/when it occurs. To do that effectively, image quality data need to be constantly gathered and processed. A common approach is to print and measure test-patterns over-time at a pre-determined schedule and then analyze the measured image quality data to discover/detect changes. But due to the presence of other printer noise, such as page-to-page instability, mottle etc., it is likely that the measured image quality data for a given image quality attribute of interest (e.g. streaks) at a given time is governed by a statistical model rather than a deterministic one. This imposes difficulty for methods intended to detect image quality changes reliably unless sufficient data of test samples are collected. However, these test samples are non-value- add to the customers and should be minimized. An alternative is to directly measure and assess the image quality attributes of interest from customer pages and post-processing them for detecting changes. In addition to the difficulty caused by sources of other printer noise, variable image contents from customer pages further impose challenges in the change detection. This paper addresses these issues and presents a feasible solution in which change points are detected by statistical model-ranking.

Ink-saving strategies for CMYK printers have evolved from their earlier stages where the 'draft' print mode was the main option available to control ink usage. The savings were achieved by printing alternate dots in an image at the expense of reducing print quality considerably. Nowadays, customers are not only unwilling to compromise quality but have higher expectations regarding both visual print quality and ink reduction solutions. Therefore, the need for more intricate ink-saving solutions with lower impact on print quality is evident. Printing-related factors such as the way the printer places the dots on the paper and the ink-substrate interaction play important and complex roles in the characterization and modeling of the printing process that make the ink reduction topic a challenging problem. In our study, we are interested in benchmarking ink-saving algorithms to find the connections between different ink reduction levels of a given ink-saving method and a set of print quality attributes. This study is mostly related to CMYK printers that use dispersed dot halftoning algorithms. The results of our efforts to develop such an evaluation scheme are presented in this paper.

Banding is a well-known artifact produced by printing systems. It usually appears as lines perpendicular to the process direction of the print. Therefore, banding is an important print quality issue which has been analyzed and assessed by many researchers. However, little literature has focused on the study of the masking effect of content for this kind of print quality issue. Compared with other image and print quality research, our work is focused on the print quality of typical documents printed on a digital commercial printing press. In this paper, we propose a Masking Mediated Print Defect Visibility Predictor (MMPDVP) to predict the visibility of defects in the presence of customer content. The parameters of the algorithm are trained from ground-truth images that have been marked by subjects. The MMPDVP could help the press operator decide whether the print quality is acceptable for specific customer requirements. Ultimately, this model can be used to optimize the print-shop workflow.

Observing and evaluating print defects represents a major challenge in the area of print quality research. Visual identification and quantification of these print defects becomes a key issue for improving print quality. However, the page content may confound the visual evaluation of print defects in actual printouts. Our research is focused on banding in the presence of print content in the context of commercial printing. In this paper, a psychophysical experiment is described to evaluate the perception of bands in the presence of print content. A number of banding defects are added by way of simulation to a selected set of commercial print contents to form our set of stimuli. The participants in the experiment mark these stimuli based on their observations via a graphical user interface (GUI). Based on the collection of the marked stimuli, we were able to see general consistency among different participants. Moreover, the results showed that the likelihood of an observer perceiving the banding defect in a smooth area is much higher than in a high frequency area. Furthermore, our results also indicate that the luminance of the image may locally affect the visibility of the print defects to some degree.

The paper contributes to No-Reference video quality assessment of broadcasted HD video over IP networks and DVB. In this work we have enhanced our bottom-up spatio-temporal saliency map model by considering semantics of the visual scene. Thus we propose a new saliency map model based on face detection that we called semantic saliency map. A new fusion method has been proposed to merge the bottom-up saliency maps with the semantic saliency map. We show that our NR metric WMBER weighted by the spatio-temporal-semantic saliency map provides higher results then the WMBER weighted by the bottom-up spatio-temporal saliency map. Tests are performed on two H.264/AVC video databases for video quality assessment over lossy networks.

This paper presents an approach using an extended model for the linkage of Quality of Experience with the technical realization, making use of algorithmic developments in the field of free viewpoint video. The interlinking model of Quality of Experience with the technical realization is implemented by taking into account subjective evaluation results as well as possible variances of algorithmic processes. This is used to support the scalability and adaptability of the system based on the end users' requirements. The problem is a missing link bringing together quality perception and algorithm design in a quantitative and practical way. An extended model is defined after a detailed literature review (taking into account existing approaches) showing the lack of an adequate way to link Quality of Experience with algorithmic developments. The presented model includes prior evaluation activities on the subjective quality assessment of free viewpoint video objects used within the context of video communication to support eye contact. However, quality estimation in this particular use case has not been covered yet, and adequate approaches are missing. A methodological approach to define quality influencing factors, and its results, are presented. An exemplary extended model using a taxonomy approach is introduced. A description in detail of the interlinking model taking into account existing evaluation results is given, and a way of weighting quality influencing factors therefore is outlined.

In H.264/AVC rate control algorithm, the bit allocation process and the QP determination are not optimal. At frame layer, there is an implicit assumption considering that the video sequence is more or less stationary and consequently the neighbouring frames have similar characteristics. So, the target Bit-Rate for each frame is estimated using a straightforward process that allocates an equal bit budget for each frame regardless of its temporal and spatial complexities. This uniform allocation is surely not suited especially for all types of video sequences. The target bits determination at macroblock layer uses the MAD (Mean Absolute Difference) ratio as a complexity measure in order to promote interesting macroblocks, but this measure remains inefficient in handling macroblock characteristics. In a previous work we have proposed Rate-Quantization (R-Q) models for Intra and Inter frames used to deal with the QP determination shortcoming. In this paper, we look to overcome the limitation of the bit allocation process at the frame and the macroblock layers. At the frame level, we enhance the bit allocation process by exploiting frame complexity measures. Thereby, the target bit determination for P-frames is adjusted by combining two temporal measures: The first one is a motion ratio determined from actual bits used to encode previous frames. The second measure exploits both the difference between two consecutive frames and the histogram of this difference. At macroblock level, the visual saliency is used in the bit allocation process. The basic idea is to promote salient macroblocks. Hence, a saliency map, based on a Bottom-Up approach, is generated and a macroblock classification is performed. This classification is then used to accurately adjust UBits_H264 which represents the usual bit budget estimated by H.264/AVC bit allocation process. For salient macroblocks the adjustment leads to a bit budget which is always larger than UBits_H264. The extra bits added to code these macroblocks are deducted from the bit budget allocated to the non-salient macroblocks. Simulations have been carried out using JM15.0 reference software, several video sequences and different target Bit-Rates. In comparison with JM15.0 algorithm, the proposed approach improves the coding efficiency in terms of PSNR/PSNR-HVS (up to +2dB/+3dB). Furthermore, the bandwidth constraint is always satisfied because the actual Bit-Rate is always lower than or equal to the target Bit-Rate.

To ensure that video communication services meet the high expectations of end users, user quality of experience (QoE) must be properly considered. Therefore, various methods to assess QoE of video services have been proposed. However, several QoE assessment methods based on user motivation show that video quality is not the only perspective for QoE. To assess the QoE of video communication services, we need to obtain "user preferences" in which user interest in a video must be considered in addition to video quality, motivation, and level of motivation achievement. Additionally, we consider multiple QoE factors, such as preference and motivation achievement level, which vary for each participant. We propose a QoE assessment method for mobile video services. We provide various motivations to participants motivation before they watch videos on mobile devices. After watching, participants assess QoE for video quality, motivation achievement level, and user preference. Simultaneously, participants assess integrated QoE (IQoE), which refers to user satisfaction. We conducted an experiment using the proposed method. From the results, we concluded that taking user preference into consideration is important for QoE assessment methods based on motivation. We also clarified that the video quality level required to meet certain user expectations depends on the classification of participants.

Computer generated images are common in numerous computer graphics applications such as games, modeling, and simulation. There is normally a tradeoff between the time allocated to the generation of each image frame and and the quality of the image, where better quality images require more processing time. Specifically, in the rendering of 3D objects, the surfaces of objects may be manipulated by subdividing them into smaller triangular patches and/or smoothing them so as to produce better looking renderings. Since unnecessary subdivision results in increased rendering time and unnecessary smoothing results in reduced details, there is a need to automatically determine the amount of necessary processing for producing good quality rendered images. In this paper we propose a novel supervised learning based methodology for automatically predicting the quality of rendered images of 3D objects. To perform the prediction we train on a data set which is labeled by human observers for quality. We are then able to predict the quality of renderings (not used in the training) with an average prediction error of roughly 20%. The proposed approach is compared to known techniques and is shown to produce better results.

This paper reviews the perception of image quality by human respondents in the context of super-resolution. Specifically, the desirability of an automated mechanism for generating a metric that correlates well with human perceptions of image quality is discussed. Common metrics are evaluated to ascertain whether they demonstrate suitable correlation with human perception of image quality. It is found that the commonly used pixel-difference evaluation technique outperforms a threshold-based technique; however, neither is demonstrated to correlate well with human perception.

In this paper, we introduce a concept of the image quality metamerism as an expanded version of the metamerism defined in the color science. The concept is used to unify different image quality attributes, and applied to introduce a metric showing the degree of image quality metamerism to analyze a cultural property. Our global goal is to build a metric to evaluate total quality of images acquired by different imaging systems and observed under different viewing conditions. As the basic step to the global goal, the metric is consisted of color, spectral and texture information in this research, and applied to detect image quality metamers to investigate the cultural property. The property investigated is the oldest extant version of folding screen paintings that depict the thriving city of Kyoto designated as a nationally important cultural property in Japan. Gold colored areas painted by using high granularity colorants compared with other color areas in the property are evaluated based on the metric, then the metric is visualized as a map showing the possibility of the image quality metamer to the reference pixel.

In many color measurement applications, such as those for color calibration and profiling, "patch code" has been used successfully for job identification and automation to reduce operator errors. A patch code is similar to a barcode, but is intended primarily for use in measurement devices that cannot read barcodes due to limited spatial resolution, such as spectrophotometers. There is an inherent tradeoff between decoding robustness and the number of code levels available for encoding. Previous methods have attempted to address this tradeoff, but those solutions have been sub-optimal. In this paper, we propose a method to design optimal patch codes via device characterization. The tradeoff between decoding robustness and the number of available code levels is optimized in terms of printing and measurement efforts, and decoding robustness against noises from the printing and measurement devices. Effort is drastically reduced relative to previous methods because print-and-measure is minimized through modeling and the use of existing printer profiles. Decoding robustness is improved by distributing the code levels in CIE Lab space rather than in CMYK space.

Today in many homes big TV screens and hifi systems are common. But is the perception of subjective video quality under professional test conditions the same as at home? Therefore we examined two things: How large is the influence of the presentation device but also the influence of the soundtrack, both in HDTV (1080p50). Previous work has shown that a difference is noticeable, but there have not been studies with consumer devices, yet. It was also shown that there is an influence of the soundtrack, but only in SDTV or lower resolutions. Therefore we conducted subjective video tests: One test with different devices, a 23-inch-reference monitor, a high quality 56-inch-LCD-TV and an HD-projector, and one test in which we presented a soundtrack on a 7.1-channel hifi system in addition to the HD-projector. The results show two things: First the test subjects had a higher quality of experience with the consumer devices than with the reference monitor, although the video quality of the reference monitor itself was rated better in an additional questionnaire and the mean opinion score (MOS). The second result is that there is no significant difference in the MOS between showing the videos on the projector with or without sound.

In this contribution, we will examine two important aspects of subjective video quality assessment and their overall influence on the test results in detail: the participants' viewing experience and the quality range in the stabilization phase. Firstly, we examined if the previous viewing experience of participants in subjective tests influence the results. We performed a number of single- and double-stimulus tests assessing the visual quality of video material compressed with both H.264/AVC and MPEG2 not only at different quality levels and content, but also in different video formats from 576i up to 1080i. During these tests, we collected additional statistical data on the test participants. Overall, we were able to collect data from over 70 different subjects and analyse the influence of the subjects' viewing experience on the results of the tests. Secondly, we examined if the visual quality range presented in the stabilization phase of a subjective test has significant influence on the test results. Due to time constraints, it is sometimes necessary to split a test into multiple sessions representing subsets of the overall quality range. Consequently, we examine the influence of the quality range presented in the stabilization phase on the overall results, depending on the quality subsets included in the stabilization phase.

A fair knowledge of the human hand tremor responsible for camera-shake noise as well as a way to measure the impact of motion-blur on human-perceived image quality are mandatory to quantify the gain of image stabilization systems. In order to define specifications for the stabilization chain we have derived a perceptual image quality metric for camera-shake induced motion blur. This quality metric was validated with visual tests. Comparison to the ground-truth shows a good fitting in the simple case of straight-line motion blur as well as a fair fitting in the more complex case of arbitrary motion blur. To our best knowledge this is the first metric that can predict image quality degradation in the case of arbitrary blur. The quality model on which this metric is based gives some valuable insights on the way motion blur impacts perceived quality and can help the design of optimal image stabilization systems.

Extracting salient regions of a still image, which are pertinent areas likely to attract subjects' fixations, can be useful to adapt compression loss according to human attention. In the literature, various algorithms have been proposed for saliency extraction, ranging from region-of-interest (ROI) or point-of-interest (POI) algorithms to saliency models, which also extract ROIs. Implementing such an algorithm within image sensors implies to evaluate its complexity and performance of fixation prediction. However, there have been no pertinent criteria to compare these algorithms in predicting human fixations due to the different nature between ROIs and POIs. In this paper, we propose a novel criterion which is able to compare the prediction performance of ROI and POI algorithms. Aiming at the electronic implementation of such an algorithm, the proposed criterion is based on blocks, which is consistent with processing within image sensors. It also takes into account salient surface, an important factor in electronic implementation, to reflect more accurately the prediction performance of algorithms. The criterion is then used for comparison in a benchmark of several saliency models and ROI/POI algorithms. The results show that a saliency model, which has higher computational complexity, gives better performance than other ROI/POI algorithms.