This PDF file contains the front matter associated with SPIE Proceedings Volume 7529, including the Title Page, Copyright information, Table of Contents, and the Conference Committee listing.

Quality assessment is becoming an important issue in the framework of image processing. This need is expressed by the fact that the quality threshold of end-users has been shifted up because of the large availability of high fidelity sensors at very affordable price. This observation has been made for different application domains such as printing, compression, transmission, and so on. Starting from this, it becomes very important to manufacturers and producers to provide products of high quality to attract the consumer. This high interest on quality means that tools to measure it have to be available. This work is dedicated to the comparison of subjective methodologies in the digital cinema framework. The main goal is to determine with a group of observers, which methodology is better for assessing digital cinema content and what is the annoyance level associated to each of them. Several configurations are tested side by side, Butterfly, one by one, Horizontal scroll, vertical scroll, Horizontal and vertical scroll.

The Single Stimulus (SS) method is often chosen to collect subjective data testing no-reference objective metrics, as it is straightforward to implement and well standardized. At the same time, it exhibits some drawbacks; spread between different assessors is relatively large, and the measured ratings depend on the quality range spanned by the test samples, hence the results from different experiments cannot easily be merged . The Quality Ruler (QR) method has been proposed to overcome these inconveniences. This paper compares the performance of the SS and QR method for pictures impaired by Gaussian blur. The research goal is, on one hand, to analyze the advantages and disadvantages of both methods for quality assessment and, on the other, to make quality data of blur impaired images publicly available. The obtained results show that the confidence intervals of the QR scores are narrower than those of the SS scores. This indicates that the QR method enhances consistency across assessors. Moreover, QR scores exhibit a higher linear correlation with the distortion applied. In summary, for the purpose of building datasets of subjective quality, the QR approach seems promising from the viewpoint of both consistency and repeatability.

Imaging systems in camera phones have image quality limitations attributed to optics, size, and cost constraints. These limitations generally result in unwanted system noise. In order to minimize the image quality degradation, nonlinear noise cleaning algorithms are often applied to the images. However, as the strength of the noise cleaning increases, this often leads to texture degradation. The Camera Phone Image Quality (CPIQ) initiative of the International Imaging Industry Association (I3A) has been developing metrics to quantify texture appearance in camera phone images. Initial research established high correlation levels between the metrics and psychophysical data from sets of images that had noise cleaning filtering applied to simulate capture in actual camera phone systems. This paper describes the subsequent work to develop a texture-based softcopy attribute ruler in order to assess the texture appearance of eight camera phone units from four different manufacturers and to assess the efficacy of the texture metrics. Multiple companies participating in the initiative have been using the softcopy ruler approach in order to pool observers and increase statistical significance. Results and conclusions based on three captured scenes and two texture metrics will be presented.

Subjective image quality data for 9 image processing pipes and 8 image contents (taken with mobile phone camera, 72 natural scene test images altogether) from 14 test subjects were collected. A triplet comparison setup and a hybrid qualitative/quantitative methodology were applied. MOS data and spontaneous, subjective image quality attributes to each test image were recorded. The use of positive and negative image quality attributes by the experimental subjects suggested a significant difference between the subjective spaces of low and high image quality. The robustness of the attribute data was shown by correlating DMOS data of the test images against their corresponding, average subjective attribute vector length data. The findings demonstrate the information value of spontaneous, subjective image quality attributes in evaluating image quality at variable quality levels. We discuss the implications of these findings for the development of sensitive performance measures and methods in profiling image processing systems and their components, especially at high image quality levels.

We present a Videospace framework for classification of selected videos with chosen user-groups, device-types or device-classes. Photospace has proven to be effective in classifying large amounts of still images via simple technical parameters. We use the measures of subject-camera distance, scene lighting and object motion to classify single videos and finally represent all videos of the chosen group in a 3-dimensional space. An expert-rated sample of video was collected to obtain an estimation of the parameters for a chosen group of videos. Sub-groups of videos were found using Videospace measures. The presented framework can be used to obtain information about technical requirements of general device use and typical shooting conditions of the end users. Future measurement efficiency and precision could be improved by using computer-based algorithms or device based measurement techniques to obtain better samples Videospace parameters. Videospace information could be used for finding the most meaningful benchmarking contexts or getting information about shooting in general with chosen devices or devices groups. Using information about typical parameters for a chosen video group, algorithm and device development can be focused on typical shooting situations, if processing power and device-size are otherwise reduced.

Manufacturers of commercial display devices continuously try to improve the perceived image quality of their products. By applying some post processing techniques on the incoming image signal, they aim to enhance the quality level perceived by the viewer. Applying such techniques may cause side effects on different portions of the processed image. In order to apply these techniques effectively to improve the overall quality, it is vital to understand how important quality is for different parts of the image. To study this effect, a three-phase experiment was conducted where observers were asked to score images which had different levels of quality in their saliency regions than in the background areas. The results show that the saliency area has a greater effect on the overall quality of the image than the background. This effect increases with the increasing quality difference between the two regions. It is, therefore, important to take this effect into consideration when trying to enhance the appearance of specific image regions.

Psychophysical image quality assessments have shown that subjective quality depended upon the pictorial content of the test images. This study is concerned with the nature of scene dependency, which causes problems in modeling and predicting image quality. This paper focuses on scene classification to resolve this issue and used K-means clustering to classify test scenes. The aim was to classify thirty two original test scenes that were previously used in a psychophysical investigation conducted by the authors, according to their susceptibility to sharpness and noisiness. The objective scene classification involved: 1) investigation of various scene descriptors, derived to describe properties that influence image quality, and 2) investigation of the degree of correlation between scene descriptors and scene susceptibility parameters. Scene descriptors that correlated with scene susceptibility in sharpness and in noisiness are assumed to be useful in the objective scene classification. The work successfully derived three groups of scenes. The findings indicate that there is a potential for tackling the problem of sharpness and noisiness scene susceptibility when modeling image quality. In addition, more extensive investigations of scene descriptors would be required at global and local image levels in order to achieve sufficient accuracy of objective scene classification.

The aim of the study was to develop a test image for print quality evaluation to improve the current state of the art in testing the quality of digital printing. The image presented by the authors in EI09 portrayed a breakfast scene, the content of which could roughly be divided in four object categories: a woman, a table with objects, a landscape picture and a gray wall. The image was considered to have four main areas of improvement: the busyness of the image, the control of the color world, the salience of the object categories, and the naturalness of the event and the setting. To improve the first image, another test image was developed. Whereas several aspects were improved, the shortcomings of the new image found by visual testing and self-report were in the same four areas. To combine the insights of the two test images and to avoid their pitfalls, a third image was developed. The goodness of the three test images was measured in subjective tests. The third test image was found to address efficiently three of the four improvement areas, only the salience of the objects left a bit to be desired.

Sorting and searching operations used for the selection of test images strongly affect the results of image quality investigations and require a high level of versatility. This paper describes the way that inherent image properties, which are known to have a visual impact on the observer, can be used to provide support and an innovative answer to image selection and classification. The selected image properties are intended to be comprehensive and to correlate with our perception. Results from this work aim to lead to the definition of a set of universal scales of perceived image properties that are relevant to image quality assessments. The initial prototype built towards these objectives relies on global analysis of low-level image features. A multidimensional system is built, based upon the global image features of: lightness, contrast, colorfulness, color contrast, dominant hue(s) and busyness. The resulting feature metric values are compared against outcomes from relevant psychophysical investigations to evaluate the success of the employed algorithms in deriving image features that affect the perceived impression of the images.

A crucial step in image compression is the evaluation of its performance, and more precisely the available way to measure the final quality of the compressed image. Usually, to measure performance, some measure of the covariation between the subjective ratings and the degree of compression is performed between rated image quality and algorithm. Nevertheless, local variations are not well taken into account. We use the recently introduced Maximum Likelihood Difference Scaling (MLDS) method to quantify suprathreshold perceptual differences between pairs of images and examine how perceived image quality estimated through MLDS changes the compression rate is increased. This approach circumvents the limitations inherent to subjective rating methods.

No-reference quality metrics estimate the perceived quality exploiting only the image itself. Typically, noreference metrics are designed to measure specific artifacts using a distortion model. Some psycho-visual experiments have shown that the perception of distortions is influenced by the amount of details in the image's content, suggesting the need for a "content weighting factor." This dependency is coherent with known masking effects of the human visual system. In order to explore this phenomenon, we setup a series of experiments applying regression trees to the problem of no-reference quality assessment. In particular, we have focused on the blocking distortion of JPEG compressed images. Experimental results show that information about the visual content of the image can be exploited to improve the estimation of the quality of JPEG compressed images.

In medical networked applications, the server-generated application view, consisting of medical image content and synthetic text/GUI elements, must be compressed and transmitted to the client. To adapt to the local content characteristics, the application view is divided into rectangular patches, which are classified into content classes: medical image patches, synthetic image patches consisting of text on a uniform/natural/medical image background and synthetic image patches consisting of GUI elements on a uniform/natural/medical image background. Each patch is thereafter compressed using a technique yielding perceptually optimal performance for the identified content class. The goal of this paper is to identify this optimal technique, given a set of candidate schemes. For this purpose, a simulation framework is used which simulates different types of compression and measures the perceived differences between the compressed and original images, taking into account the display characteristics. In a first experiment, JPEG is used to code all patches and the optimal chroma subsampling and quantization parameters are derived for different content classes. The results show that 4:4:4 chroma subsampling is the best choice, regardless of the content type. Furthermore, frequency dependant quantization yields better compression performance than uniform quantization, except for content containing a significant number of very sharp edges. In a second experiment, each patch can be coded using JPEG, JPEG XR or JPEG 2000. On average, JPEG 2000 outperforms JPEG and JPEG XR for most medical images and for patches containing text. However, for histopathology or tissue patches and for patches containing GUI elements, classical JPEG compression outperforms the other two techniques.

Small digital camera modules such as those in mobile phones have become ubiquitous. Their low-light performance is of utmost importance since a high percentage of images are made under low lighting conditions where image quality failure may occur due to blur, noise, and/or underexposure. These modes of image degradation are not mutually exclusive: they share common roots in the physics of the imager, the constraints of image processing, and the general trade-off situations in camera design. A comprehensive analysis of failure modes is needed in order to understand how their interactions affect overall image quality. Low-light performance is reported for DSLR, point-and-shoot, and mobile phone cameras. The measurements target blur, noise, and exposure error. Image sharpness is evaluated from three different physical measurements: static spatial frequency response, handheld motion blur, and statistical information loss due to image processing. Visual metrics for sharpness, graininess, and brightness are calculated from the physical measurements, and displayed as orthogonal image quality metrics to illustrate the relative magnitude of image quality degradation as a function of subject illumination. The impact of each of the three sharpness measurements on overall sharpness quality is displayed for different light levels. The power spectrum of the statistical information target is a good representation of natural scenes, thus providing a defined input signal for the measurement of power-spectrum based signal-to-noise ratio to characterize overall imaging performance.

Several measurable image quality attributes contribute to the perceived resolution of a printing system. These contributing attributes include addressability, sharpness, raggedness, spot size, and detail rendition capability. This paper summarizes the development of evaluation methods that will become the basis of ISO 29112, a standard for the objective measurement of monochrome printer resolution.

A mathematical model of the electrophotographic printing process has been developed. This model can be used for analysis. From this a print simulation process has been developed to simulate the effects of the model components on toner particle placement. A wide variety of simulated prints are produced from the model's three main inputs, laser spread, charge to toner proportionality factor and toner particle size. While the exact placement of toner particles is a random process, the total effect is not. The effect of each model parameter on the ISO 13660 print quality attributes line width, fill, raggedness and blurriness is described.

The techniques of one-dimensional projection in the spatial domain and contrast sensitivity function (CSF) are generally used to measure banding. Due to the complex printing process of laser printers, hardcopy prints contain other 2D nonuniformities such as graininess and mottle besides banding. The method of 1D projection is useful for extracting banding, but it induces the confounding effect of graininess or mottle on the measurement of perceived banding. The appearance of banding in laser printers is more similar to the sum of various rectangular signals having different amplitudes and frequencies. However, in many cases banding is modeled as a simple sinusoidal signal and the CSF is frequently applied. In this paper, we propose new measurement method of banding well correlated with human perception. Two kinds of spatial features give a good performance to banding measurement. First the correlation factor between two adjacent 1D signals is considered to obtain banding power which reduces the confounding effect of graininess and mottle. Secondly, a spatial smoothing filter is designed and applied to reduce the less perceptible low frequency components instead of using the CSF. By using moving window and subtracting the local mean values, the imperceptible low frequency components are removed while the perceptible low frequency components like the sharp edge of rectangular waves are preserved. To validate the proposed method, psychophysical tests are performed. The results show that the correlations between the proposed method and the perceived scales are 0.96, 0.90, and 0.95 for black, cyan, and magenta, respectively.

The goal of the study was to develop a method for quality computation of digitally printed images. We wanted to use only the attributes which have a meaning for subjective visual quality experience of digitally printed images. Based on the subjective data and our assessments the attributes for quality calculation were sharpness, graininess and color contrast. The proposed graininess metric divides the fine detail image into blocks and used the low energy blocks for graininess calculation. The proposed color contrast metric computes the contrast of dominant colors using the coarse scale image. The proposed sharpness metric divides the coarse scale image into blocks and uses the high energy blocks for sharpness calculation. The reduced reference features of sharpness and graininess metrics are the numbers of high or low energy blocks. The reduced reference features of the color contrast metric are the directions of the dominant colors in reference image. The overall image quality was calculated by combining the values. The performance of proposed application specific image quality metric was high compared to the state of the art reduced reference applicationindependent image quality metric. Linear correlation coefficients between subjective and predicted MOS were 0.88 for electrophotography and 0.98 for ink-jet printed samples, for a sample set of 21 prints for electrophotography and for inkjet, subjectively evaluated by 28 observers.

Since typical document scanners are based on the principle of comparing the amount of light reflected from point of interest with that of a known surface, the reflectance of point of interest on the document measured by a scanner is therefore impacted by the reflectance of its neighboring region via multiple light reflections. The effect was referred to as the "integrating cavity effect". We investigate the effect by establishing an optical model with optical ray tracing. We demonstrated the effect by examining the illumination profile after accounting for multiple reflections off the document. The simulation shows that the impact is less significant for the slow scan direction as opposed to the fast scan direction. We identified that the platen glass can contribute just as much as the illumination assembly to the effect but the impact is much more localized. Further we demonstrated that the impact of the illumination assembly and the platen glass is independent from each other to a great degree and the impact of the illumination assembly to the illumination profile is equivalent to a convolution of the original document content with a Gaussian kernel of a considerable band width.

Extended Depth of Focus technologies are well known in the literature, and in recent years this technology has made its way into camera phones. While the fundamental approach might have significant advantages over conventional technologies, often in practice, it turns out the results can be accompanied by undesired artifacts that are hard to quantify. In order to conduct an objective comparison with the conventional focus technology, new methods need to be devised that are able to quantify not only the quality of focus but also the artifacts introduced by the use of EDOF methods. In this paper we propose a test image and a methodology to quantify focus quality and its dependence on the distance. Our test image is created from a test image element that contains different shapes to measure frequency response.

Noise reduction in the image processing pipeline of digital cameras has a huge impact on image quality. It may result in loss of low contrast fine details, also referred to as texture blur.Previous papers have shown, that the objective measurement of the statistical parameter kurtosis in a reproduction of white Gaussian noise with the camera under test correlates well with the subjective perception of these ramifications. To get a more detailed description of the influence of noise reduction on the image, we compare the results of different approaches to measure the spatial frequency response (SFR). Each of these methods uses a different test target, therefore we get different results in the presence of adaptive filtering. We present a study on the possibility to derive a detailed description of the influence of noise reduction on the different spatial frequency sub bands based on the differences of the measured SFR using several approaches. Variations in the underlying methods have a direct influence on the derived measurements, therefore we additionally checked for the differences of all used methods.

The evaluation of the noise present in the image acquisition system and the influence of the noise is essential to image acquisition. However the mean square errors (MSE) is not divided into two terms, i.e., the noise independent MSE (MSE_free) and noise dependent MSE (MSE_noise) were not discussed separately before. The MSE_free depends on the spectral characteristics of a set of sensors, illuminations and reflectances of imaged objects and the MSE_free arises in the noise free case, however MSE_noise originates from the noise present image acquisition system. One of the authors (N.S.) already proposed a model to separate the MSE into the two factors and also proposed a model to estimate noise variance present in image acquisition systems. By the use of this model, we succeeded in the expression of the MSE_noise as a function of the noise variance and showed that the experimental results agreed fairly well with the expression when the Wiener estimation was used for the recovery. The present paper shows the extended expression for the influence of the system noise on the MSE_noise and the experimental results to show the trustworthiness of the expression for the regression model, Imai-Berns model and finite dimensional linear model.

Automatic portrait enhancement by attenuating skin flaws (pimples, blemishes, wrinkles, etc.) has received considerable attention from digital camera manufacturers thanks to its impact on the public. Subsequently, a number of algorithms have been developed to meet this need. One central aspect to developing such an algorithm is quality assessment: having a few numbers that precisely indicate the amount of beautification brought by an algorithm (as perceived by human observers) is of great help, as it works on circumvent time-costly human evaluation. In this paper, we propose a method to numerically evaluate the quality of a skin beautification algorithm. The most important aspects we take into account and quantize to numbers are the quality of the skin detector, the amount of smoothing performed by the method, the preservation of intrinsic skin texture, and the preservation of facial features. We combine these measures into two numbers that assess the quality of skin detection and beautification. The derived measures are highly correlated with human perception, therefore they constitute a helpful tool for tuning and comparing algorithms.

Remote sensing is widely used to assess the destruction from natural disasters and to plan relief and recovery operations. How to automatically extract useful features and segment interesting objects from digital images, including remote sensing imagery, becomes a critical task for image understanding. Unfortunately, the data collection of aerial digital images is constrained with bad weather, muzzy atmosphere, and unstable camera or camcorder. As a result, remote sensing imagery is shown as lowcontrast, blurred, and dark from time to time. Here, we introduce a new method integrating image local statistics and image natural characteristics to enhance remote sensing imagery. This method computes the adaptive histogram equalization to each distinct region of the input image and then redistributes the lightness values of the image. The natural characteristic of image is applied to adjust the restoration contrast. The experiments on real data show the effectiveness of the algorithm.

Pan-sharpened images can effectively be used in various remote sensing applications. During recent years a vast number of pan-sharpening algorithms has been proposed. Thus, the evaluation of their performance became a vital issue. The quality assessment of pan-sharpened images is complicated by the absence of reference data, the ideal image what the multispectral scanner would observe if it had as high spatial resolution as the panchromatic instrument. This paper presents a novel method to evaluate the degree of local quality degradation in pansharpened images, which is the result of contrast inversion of the fusing bands. The proposed method does not require a reference image. Firstly, the algorithm identifies the areas in which the contrast inversion may be confidently detected. Then, based on the found spatial consistency violations, the quantitative degradation index is calculated for the fused product. The proposed approach was validated with the use of very high resolution optical imagery. The experiments have shown that the proposed measure objectively reflects local quality deterioration of pan-sharpened images.

We consider two physical systems where overlapped displays are employed: (1) Wobulation -a single projector that rapidly shifts the entire display in time by a subpixel amount; (2) Several projector displays overlaid in space with a complex array of space-varying subpixel offsets. In this work we focus on quantifying the resolution increase of these approaches over that of a single projector. Because of the nature of overlapping projections with different degrees of prospective distortion, overlaid pixels have space-varying offsets in both dimensions. Our simulator employs the perspective transformation or homography associated with the particular projector geometry for each subframe. The resulting simulated displays are stunningly accurate. We use "grill" patterns to assess the resolution performance that vary in period, phase, and orientation. A new Fourier-based test procedure is introduced that generates repeatable results that eliminate problems due to phase and spatial variation. We report on results for 2 and 4 position wobulation, and for 1, 2, 4, and 10 overlaid projectors using the frequency-domain based contrast modulation metric. The effects of subpixel phase are illustrated for various grill periods. The results clearly show that resolution performance is indeed improved for overlapped displays.

The technology of scanning-backlight can effectively reduce motion blur in LCD, but reintroduce large area flicker phenomenon. Perception experiments were performed to study the flicker visibility in a scanning-backlight LCD system. Different operational modes of the scanning-backlight were used to generate different light performance. Five color blocks: red, green, blue, white and yellow were chosen as experimental stimuli to check the influence of color on flicker visibility in the most strict situation. Two natural images, for each with a colorful version, a black-and-white version, together with a uniform white block (without any details) with the same average luminance of the natural image, were adopted to verify the influence of color and details in image content on flicker visibility in normal viewing situation. Results show that, color has no statistically significant influence on flicker visibility when luminance profiles are similar. And details in image content can effectively decrease sensitivity to flicker visibility, which could because details can distract viewer's attention away from flicker perception.

This paper proposes an approach to improve the performance of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) for image quality assessment in digital cinema applications. Based on the particularities of quality assessment in a digital cinema setup, some attributes of the human visual system (HVS) are taken into consideration, including the fovea acuity angle and contrast sensitivity, combined with viewing conditions in the cinema to select appropriate image blocks for calculating the perceived quality by PSNR and SSIM. Furthermore, as the HVS is not able to perceive all the distortions because of selective sensitivities to different contrasts, and masking always exists, we adopt a modified PSNR by considering the contrast sensitivity function and masking effects. The experimental results demonstrate that the proposed approach can evidently improve the performance of image quality metrics in digital cinema applications.

In this paper we propose a methodology for TV set verification, intended for detecting picture quality degradation and functional failures within a TV set. In the proposed approach we compare the TV picture captured from a TV set under investigation with the reference image for the corresponding TV set in order to assess the captured picture quality and therefore, assess the acceptability of TV set quality. The methodology framework comprises a logic block for designing the verification process flow, a block for TV set quality estimation (based on image quality assessment) and a block for generating the defect tracking database. The quality assessment algorithm is a full-reference intra-frame approach which aims at detecting various digital specific-TV-set picture degradations, coming from TV system hardware and software failures, and erroneous operational modes and settings in TV sets. The proposed algorithm is a block-based scheme which incorporates the mean square error and a local variance between the reference and the tested image. The artifact detection algorithm is shown to be highly robust against brightness and contrast changes in TV sets. The algorithm is evaluated by performance comparison with the other state-of-the-art image quality assessment metrics in terms of detecting TV picture degradations, such as illumination and contrast change, compression artifacts, picture misalignment, aliasing, blurring and other types of degradations that are due to defects within the TV set video chain.

We propose a quality-aware computational optimization of inpainting based upon the intelligent application of a battery of inpainting methods. By leveraging the Decision-Action-Reward Network (DARN) formalism and a bottom-up model of human visual attention, methods are selected for optimal local use via an adjustable quality-time tradeoff and (empirical) training statistics aimed at minimizing observer foveal attention to inpainted regions. Results are shown for object removal in high-resolution consumer video, including a comparison of output quality and efficiency with homogeneous inpainting applications.

The effect of video compression is examined using the task-based performance metrics of the new Video National Intelligence Interpretability Rating Scale (Video NIIRS). Video NIIRS is a subjective task criteria scale similar to the well-known Visible NIIRS used for still image quality measurement. However, each task in the Video NIIRS includes a dynamic component that requires video of sufficient spatial and temporal resolution. The loss of Video NIIRS due to compression is experimentally measured for select cases. The results show that an increase in the compression and an associated increase in artifacts reduces task based interpretability and lowers the Video-NIIRS rating of the video clips. The extent of the effect has implications for system design.

Image compression techniques such as JPEG and MPEG induce losses of image quality. Representative specifications are blocking artifact, color bleeding, and smearing. These losses are usually investigated on the spatial distortions from reconstructed images such as MSE(mean square error) and PSNR(peak signal to noise ratio). However, color information is practically influenced by compression techniques. The distortion of color information is shown as distorted information of gamut characteristics such as gamut size in the reconstructed images. Accordingly, this paper introduces the investigation of the relationship between image compression and the gamut characteristics for MPEG-2 compression. Some image quality metrics are introduced; gamut size and gamut fidelity using unique color and CDI (color distribution index), respectively. The influence of moving object is first observed with time sequential. Then, deterioration due to the variation of bit rate is observed using gamut size and gamut characteristics. Results shows the moving objects do not influence a lot to the gamut characteristic, however, the decrease of bit rate gives lots of deterioration for gamut characteristics shown as the variation of CDI.

The IP television services quality is a critical issue because of the nature of transport infrastructure. Packet loss is the main cause of service degradation in such kind of network platforms. The use of forward error correction (FEC) techniques in the application layer (AL-FEC), between the source of TV service (video server) and the user terminal, seams to be an efficient strategy to counteract packet losses alternatively or in addiction to suitable traffic management policies (only feasible in "managed networks"). A number of AL-FEC techniques have been discussed in literature and proposed for inclusion in TV over IP international standards. In this paper a performance evaluation of the AL-FEC defined in SMPTE 2022-1 standard is presented. Different typical events occurring in IP networks causing different types (in terms of statistic distribution) of IP packet losses have been studied and AL-FEC performance to counteract these kind of losses have been evaluated. The performed analysis has been carried out in view of fulfilling the TV services QoS requirements that are usually very demanding. For managed networks, this paper envisages a strategy to combine the use of AL-FEC with the set-up of a transport quality based on FEC packets prioritization. Promising results regard this kind of strategy have been obtained.

In this paper the degraded video with blur and noise is enhanced by using an algorithm based on an iterative procedure. In this algorithm at first we estimate the clean data and blur function using Newton optimization method and then the estimation procedure is improved using appropriate denoising methods. These noise reduction techniques are based on local statistics of clean data and blur function. For estimated blur function we use LPA-ICI (local polynomial approximation - intersection of confidence intervals) method that use an anisotropic window around each point and obtain the enhanced data employing Wiener filter in this local window. Similarly, to improvement the quality of estimated clean video, at first we transform the data to wavelet transform domain and then improve our estimation using maximum a posterior (MAP) estimator and local Laplace prior. This procedure (initial estimation and improvement of estimation by denoising) is iterated and finally the clean video is obtained. The implementation of this algorithm is slow in MATLAB1 environment and so it is not suitable for online applications. However, MATLAB has the capability of running functions written in C. The files which hold the source for these functions are called MEX-Files. The MEX functions allow system-specific APIs to be called to extend MATLAB's abilities. So, in this paper to speed up our algorithm, the written code in MATLAB is sectioned and the elapsed time for each section is measured and slow sections (that use 60% of complete running time) are selected. Then these slow sections are translated to C++ and linked to MATLAB. In fact, the high loads of information in images and processed data in the "for" loops of relevant code, makes MATLAB an unsuitable candidate for writing such programs. The written code for our video deblurring algorithm in MATLAB contains eight "for" loops. These eighth "for" utilize 60% of the total execution time of the entire program and so the runtime should be potentially decreased by considering these loops in C. However, after implementing eighth "for" in C using the MEX library, the measured run time unexpectedly increased. According to the timing in MEX, this runtime increscent is not because of connecting MATLAB to MEX but it is related to loops. Our simulation shows implementation of a loop in C++ takes two times more than the same loop in MATLAB. In spite of C functions, the OpenCV2 functions take less time, so OpenCV that is an open source computer vision library written in C and C++ (and it is an active development on interfaces for MATLAB) would be useful for getting more speed. After implementation the "for" loops of our algorithm using OpenCV library, our simulations show the run time decreases a lot.

Active near-infrared illumination may be used in a face recognition system to achieve invariance to changes of the visible illumination. Another benefit of active near-infrared illumination is the bright pupil effect which can be used to assist eye detection. But long time exposure to near-infrared radiation is hazardous to the eyes. The level of illumination is therefore limited by potentially harmful effects to the eyes. Image sensors for face recognition under active near-infrared illumination have therefore to be carefully selected to provide optimal image quality in the desired field of application. A model of the active illumination source is introduced. Safety issues with regard to near-infrared illumination are addressed using this model and a radiometric analysis. From the illumination model requirements on suitable imaging sensors are formulated. Standard image quality metrics are used to assess the imaging device performance under application typical conditions. The characterization of image quality is based on measurements of the Opto-Electronic Conversion Function, Modulation Transfer Function and noise. A methodology to select an image sensor for the desired field of application is given. Two cameras with low-cost image sensors are characterized using the key parameters that influence the image quality for face recognition.

A new Full-Reference Singular Value Decomposition (SVD) based Image Quality Measurement (IQM) is proposed in this paper. Most of the recently developed IQMs that have been designed for measuring universal distortion types have worse results in measuring blur type distortions. The proposed method A-SVD aims at capturing the loss of structural content instead of measuring the distortion of pixel intensity value. A-SVD uses the change in the angle between the principal singular vectors as a distance between the original and distorted image blocks. Experiments were conducted using the LIVE database. The proposed algorithm was compared with another recently proposed SVD based method named M-SVD and other well-established methods including SSIM, MSSIM, and VSNR. Results have shown that the proposed method has an advantage in discerning blurry types of image distortions while providing comparable results for other distortion types. Also, the proposed method provides better linear correlation with the human score, which is a desirable attribute for the IQM to be used in other applications.

The present work concerns the development of a no-reference demosaicing quality metric. The demosaicing operation converts a raw image acquired with a single sensor array, overlaid with a color filter array, into a full-color image. The most prominent artifact generated by demosaicing algorithms is called zipper. In this work we propose an algorithm to identify these patterns and measure their visibility in order to estimate the perceived quality of rendered images. We have conducted extensive subjective experiments, and we have determined the relationships between subjective scores and the proposed measure to obtain a reliable no-reference metric.

Digital hologram sequences have great potential for the recording of 3D scenes of moving macroscopic objects as their numerical reconstruction can yield a range of perspective views of the scene. Digital holograms inherently have large information content and lossless coding of holographic data is rather inefficient due to the speckled nature of the interference fringes they contain. Lossy coding of still holograms and hologram sequences has shown promising results. By definition, lossy compression introduces errors in the reconstruction. In all of the previous studies, numerical metrics were used to measure the compression error and through it, the coding quality. Digital hologram reconstructions are highly speckled and the speckle pattern is very sensitive to data changes. Hence, numerical quality metrics can be misleading. For example, for low compression ratios, a numerically significant coding error can have visually negligible effects. Yet, in several cases, it is of high interest to know how much lossy compression can be achieved, while maintaining the reconstruction quality at visually lossless levels. Using an experimental threshold estimation method, the staircase algorithm, we determined the highest compression ratio that was not perceptible to human observers for objects compressed with Dirac and MPEG-4 compression methods. This level of compression can be regarded as the point below which compression is perceptually lossless although physically the compression is lossy. It was found that up to 4 to 7.5 fold compression can be obtained with the above methods without any perceptible change in the appearance of video sequences.