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Probability density functions for the components of the first and second derivatives of localized phase are derived.

The derivation of bounds on the structural similarity (SSIM) index as a function of quantization rate for fixed-rate uniform quantization is discussed. Findings reveal the importance of the proposed bounds for rate allocation problems in practical image and video coding applications.

An algorithm for designing linear equalizers, which maximizes the structural similarity (SSIM) index between the reference and restored signals, is proposed. Findings reveal the achievement of higher SSIM index and superior perceptual quality.

A foveated, multifixating strategy is presented for locating corners in natural images. The results have shown that the algorithm performs well on strong edges with sharp corners and less well in areas of fine detail.

A performance evaluation study of ten image quality assessment (QA) algorithms are presented, which involved 779 distorted images derived from 29 source images using five different distortion types. The study was diverse in terms of image content, distortion types, distortion strength and the number of human subjects ranking each image, and results and data from the study are made publicly available to facilitate future research in image quality assessment.

The concept of extracting features of the original high-quality images and embedding into the image data as invisible hidden messages and can provide an objective measure of the quality of the distorted images known as quality-aware image is described. A practical quality-aware image encoding, decoding and quality analysis system is formulated which uses a unique reduced-reference image quality assessment technique based on a statistical model of natural images and quantization watermarking-based data hiding method in wavelet transform domain.

The goal of quality assessment (QA) research is to design algorithms that can automatically assess the quality of images or videos in a perceptually consistent manner. The problem of image QA is approached by proposing a novel information fidelity criterion that is based on natural scene statistics.

A model that accounts for the properties of the imaging system and of the tissues is developed to get a better understanding of confocal reflectance microscopy images of cervical tissues, and to supply a basis for parameter selection in a classification algorithm. The algorithm was applied to an extensive set of cervical images and it was found that it detects 90% of hand-segmented nuclei with an average of 6 false positive per frame.

Theorems are developed that place limits on the point-wise approximation of the responses of filters, both linear shift invariant (LSI) and linear shift variant (LSV) to input signals and images that are LSV. The application of the theory was found to defoveation of images, deblurring of shift-variant blurs, and shift-variant edge detection.

A study of the objective method for assessing perceptual image quality that attempted to quantify the visibility of errors between distorted and reference images according to known characteristics of the human visual system (HVS) is presented. The development of the structural similarity index (SSIM) for quality assessment based on the assumption that HVS is highly adapted to extract structural information is discussed.

A wavelet-based, scalable video coding system, foveation scalable video coding (FSVC, is described and discussed.

It is shown that formulating the nullspace-based approach to eigenstructure-based direct multichannel blind image restoration methods implies a subspace-based approach that relies on matrix operations. The new approach has the same advantages as the nullspace-based one but requires less computational complexity.

An embedded foveation image coding (EFIC) algorithm is proposed. The algorithm orders the encoded bitstream to optimize foveated visual quality at arbitrary bit rates.

Multidimensional multicomponent and quasi-Eigenfunction approximation AM-FM models are discussed. Techniques on several images of general interest in practical applications have been demonstrated. Reconstructions that establish validity of characterizing the type of images as sums of locally narrowband modulated components have been made. Two discrete computational paradigms were used. Dominant component analysis does one thing and channelized components analysis does another.

Natural scene statistics (NSS) is used for blindly measuring the quality of images compressed by JPEG2000 image coder. An average error of 8.05 is achieved from the mean opinion scores.

Foveation has been investigated as a tool for exploring techniques for optimal rate control of foveated video. It is concluded that foveated compression and rate control offers significant potential with regard to compression performance, speed and visual quality.

Partial differential equations and AM-FM image modeling was used to achieve an automated method for repairing broken, occluded oriented image textures.

An AM-FM surface model was developed to study the projection of surface patterns seen through a binocular camera system. The proposed model also proved to be useful in the design of a modulation-based stereo matching framework where local image phase was used to compute stereo disparities. The combined concepts of the phase-based matching and AM-FM image model and use powerful multichannel Gabor image demodulation techniques led to the development of a systematic approach for coarse-to-coarse disparity computation of disparities in stereoscopic images.

Novel smoothing constraints have been developed for use in the regularized image restoration problem with the aid of two broad classes of physically significant models. The models, called piecewise image models and local image models, capture distinct image properties that can be adapted to the image and that reflect structurally important surface properties. Numerical results are presented to illustrate the mechanism of model selection, image restoration and parameter selection.

We propose a natural scene statistic-based distortion-generic blind/no-reference (NR) image quality assessment (IQA) model that operates in the spatial domain. The new model, dubbed blind/referenceless image spatial quality evaluator (BRISQUE) does not compute distortion-specific features, such as ringing, blur, or blocking, but instead uses scene statistics of locally normalized luminance coefficients to quantify possible losses of “naturalness” in the image due to the presence of distortions, thereby leading to a holistic measure of quality. The underlying features used derive from the empirical distribution of locally normalized luminances and products of locally normalized luminances under a spatial natural scene statistic model. No transformation to another coordinate frame (DCT, wavelet, etc.) is required, distinguishing it from prior NR IQA approaches. Despite its simplicity, we are able to show that BRISQUE is statistically better than the full-reference peak signal-to-noise ratio and the structural similarity index, and is highly competitive with respect to all present-day distortion-generic NR IQA algorithms. BRISQUE has very low computational complexity, making it well suited for real time applications. BRISQUE features may be used for distortion-identification as well. To illustrate a new practical application of BRISQUE, we describe how a nonblind image denoising algorithm can be augmented with BRISQUE in order to perform blind image denoising. Results show that BRISQUE augmentation leads to performance improvements over state-of-the-art methods. A software release of BRISQUE is available online: http://live.ece.utexas.edu/research/quality/BRISQUE_release.zip for public use and evaluation. [ABSTRACT FROM AUTHOR]

Our approach to blind image quality assessment (IQA) is based on the hypothesis that natural scenes possess certain statistical properties which are altered in the presence of distortion, rendering them un-natural; and that by characterizing this un-naturalness using scene statistics, one can identify the distortion afflicting the image and perform no-reference (NR) IQA. Based on this theory, we propose an (NR)/blind algorithm—the Distortion Identification-based Image Verity and INtegrity Evaluation (DIIVINE) index—that assesses the quality of a distorted image without need for a reference image. DIIVINE is based on a 2-stage framework involving distortion identification followed by distortion-specific quality assessment. DIIVINE is capable of assessing the quality of a distorted image across multiple distortion categories, as against most NR IQA algorithms that are distortion-specific in nature. DIIVINE is based on natural scene statistics which govern the behavior of natural images. In this paper, we detail the principles underlying DIIVINE, the statistical features extracted and their relevance to perception and thoroughly evaluate the algorithm on the popular LIVE IQA database. Further, we compare the performance of DIIVINE against leading full-reference (FR) IQA algorithms and demonstrate that DIIVINE is statistically superior to the often used measure of peak signal-to-noise ratio (PSNR) and statistically equivalent to the popular structural similarity index (SSIM). A software release of DIIVINE has been made available online: xlink:type="simple" xlink:href="http://live.ece.utexas.edu/research/quality/DIIVINE_release.zip" xmlns:xlink="http://www.w3.org/1999/xlink"http://live.ece.utexas.edu/research/quality/DIIVINE_release.zip for public use and evaluation. [ABSTRACT FROM AUTHOR]

We study the efficiency of deblocking algorithms for improving visual signals degraded by blocking artifacts from compression. Rather than using only the perceptually questionable PSNR, we instead propose a block-sensitive index, named PSNR-B, that produces objective judgments that accord with observations. The PSNR-B modifies PSNR by including a blocking effect factor. We also use the perceptually significant SSIM index, which produces results largely in agreement with PSNR-B. Simulation results show that the PSNR-B results in better performance for quality assessment of deblocked images than PSNR and a well-known blockiness-specific index. [ABSTRACT FROM AUTHOR]

We present an efficient method that computes dense stereo correspondences by stochastically sampling match quality values. Nonexhanstive sampling facilitates the use of quality metrics that take unique values at noninteger disparities. Depth estimates are iteratively relined with a stochastic cooperative search by perturbing the estimates, sampling match quality, and reweighting and aggregating the perturbations. The approach gains significant efficiencies when applied to video, where initial estimates are seeded using information from the previous pair in a novel application of the Z-buffering algorithm. This significantly reduces the number of search iterations required. We present a quantitative accuracy evaluation wherein the proposed method outperforms a microcanonical annealing approach by Barnard [2] and a cooperative approach by Zitnick and Kanade [27], while using fewer match quality evaluations than either. The approach is shown to have more attractive memory usage and scaling than alternatives based on exhaustive sampling. [ABSTRACT FROM AUTHOR]

There has recently been a great deal of interest in the development of algorithms that objectively measure the integrity of video signals. Since video signals are being delivered to human end users in an increasingly wide array of applications and products, it is important that automatic methods of video quality assessment (VQA) be available that can assist in controlling the quality of video being delivered to this critical audience. Naturally, the quality of motion representation in videos plays an important role in the perception of video quality, yet existing VQA algorithms make little direct use of motion information, thus limiting their effectiveness. We seek to ameliorate this by developing a general, spatio-spectrally localized multiscale framework for evaluating dynamic video fidelity that integrates both spatial and temporal (and spatio-temporal) aspects of distortion assessment. Video quality is evaluated not only in space and time, but also in space-time, by evaluating motion quality along computed motion trajectories. Using this framework, we develop a full reference VQA algorithm for which we coin the term the MOtion-based Video Integrity Evaluation index, or MOVIE index. It is found that the MOVIE index delivers VQA scores that correlate quite closely with human subjective judgment, using the Video Quality Expert Group (VQEG) FRTV Phase 1 database as a test bed. Indeed, the MOVIE index is found to be quite competitive with, and even outperform, algorithms developed and submitted to the VQEG FRTV Phase 1 study, as well as more recent VQA algorithms tested on this database. [ABSTRACT FROM AUTHOR]

We introduce a new measure of image similarity called the complex wavelet structural similarity (CW-SSIM) index and show its applicability as a general purpose image similarity index. The key idea behind CW-SSIM is that certain image distortions lead to consistent phase changes in the local wavelet coefficients, and that a consistent phase shift of the coefficients does not change the structural content of the image. By conducting four case studies, we have demonstrated the superiority of the CW-SSIM index against other indices (e.g., Dice, Hausdorff distance) commonly used for assessing the similarity of a given pair of images. In addition, we show that the CW-SSIM index has a number of advantages. It is robust to small rotations and translations. It provides useful comparisons even without a preprocessing image registration step, which is essential for other indices. Moreover, it is computationally less expensive. [ABSTRACT FROM AUTHOR]

Measurement of visual quality is of fundamental importance for numerous image and video processing applications, where the goal of quality assessment (QA) algorithms is to automatically assess the quality of images or videos in agreement with human quality judgments. Over the years, many researchers have taken different approaches to the problem and have contributed significant research in this area and claim to have made progress in their respective domains. It is important to evaluate the performance of these algorithms in a comparative setting and analyze the strengths and weaknesses of these methods. In this paper, we present results of an extensive subjective quality assessment study in which a total of 779 distorted images were evaluated by about two dozen human subjects. The "ground truth" image quality data obtained from about 25000 individual human quality judgments is used to evaluate the performance of several prominent full-reference image quality assessment algorithms. To the best of our knowledge, apart from video quality studies conducted by the Video Quality Experts Group, the study presented in this paper is the largest subjective image quality study in the literature in terms of number of images, distortion types, and number of human judgments per image. Moreover, we have made the data from the study freely available to the research community [1]. This would allow other researchers to easily report comparative results in the future. [ABSTRACT FROM AUTHOR]

Objective methods for assessing perceptual image quality traditionally attempted to quantify the visibility of errors (differences) between a distorted image and a reference image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce an alternative complementary framework for quality assessment based on the degradation of structural information. As a specific example of this concept, we develop a Structural Similarity Index and demonstrate its promise through a set of intuitive examples, as well as comparison to both subjective ratings and state-of-the-art objective methods on a database of images compressed with JPEG and JPEG2000. [ABSTRACT FROM AUTHOR]

Examines the restoration problem in noisy cases using the null space as a variety of nonlinear optimization problems with different constraints. Description of a model for a multichannel imaging system; Discussion on the implementation issues of the proposed algorithm; Review of the null space approach; Formulation of optimization problems.