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. A MATLAB implementation of the proposed algorithm is available online at http://www.cns.nyu.edu//spl sim/lcv/ssim/.

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Image quality assessment: from error visibility to structural similarity

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 25 000 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 . This would allow other researchers to easily report comparative results in the future

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A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms

Objective image and video quality measures play important roles in a variety of image and video pro- cessing applications, such as compression, communication, printing, analysis, registration, restoration, enhancement and watermarking. Most proposed quality assessment ap- proaches in the literature are error sensitivity-based meth- ods. In this paper, we follow a new philosophy in designing image and video quality metrics, which uses structural dis- tortion as an estimate of perceived visual distortion. A com- putationally ecient approach is developed for full-reference (FR) video quality assessment. The algorithm is tested on the video quality experts group (VQEG) Phase I FR-TV test data set. Keywords—Image quality assessment, video quality assess- ment, human visual system, error sensitivity, structural dis- tortion, video quality experts group (VQEG)

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Video Quality Assessment Based on Structural Distortion Measurement

Objective methods for assessing perceptual im- age quality traditionally attempt to quantify the visibility of errors (dierences) between a distorted image and a ref- erence image using a variety of known properties of the hu- man visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce an alternative com- plementary framework for quality assessment based on the degradation of structural information. As a specific exam- ple of this concept, we develop a Structural Similarity Index and demonstrate its promise through a set of intuitive ex- amples, 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. A MatLab imple- mentation of the proposed algorithm is available online at http://www.cns.nyu.edu/~lcv/ssim/.

Image Quality Assessment: From Error Measurement to Structural Similarity

This book is about objective image quality assessmentwhere the aim is to provide computational models that can automatically predict perceptual image quality. The early years of the 21st century have witnessed a tremendous growth in the use of digital images as a means for representing and communicating information. A considerable percentage of this literature is devoted to methods for improving the appearance of images, or for maintaining the appearance of images that are processed. Nevertheless, the quality of digital images, processed or otherwise, is rarely perfect. Images are subject to distortions during acquisition, compression, transmission, processing, and reproduction. To maintain, control, and enhance the quality of images, it is important for image acquisition, management, communication, and processing systems to be able to identify and quantify image quality degradations. The goals of this book are as follows; a) to introduce the fundamentals of image quality assessment, and to explain the relevant engineering problems, b) to give a broad treatment of the current state-of-the-art in image quality assessment, by describing leading algorithms that address these engineering problems, and c) to provide new directions for future research, by introducing recent models and paradigms that significantly differ from those used in the past. The book is written to be accessible to university students curious about the state-of-the-art of image quality assessment, expert industrial R&D engineers seeking to implement image/video quality assessment systems for specific applications, and academic theorists interested in developing new algorithms for image quality assessment or using existing algorithms to design or optimize other image processing applications.

Modern image quality assessment

Two experiments for evaluating psychophysical distortion metrics in Joint Photographic Experts Group (JPEG) encoded images are described. The first is a threshold experiment, in which subjects determined the bit rate or level of distortion at which distortion was just noticeable. The second is a suprathreshold experiment in which subjects ranked image blocks according to perceived distortion. The results of these experiments were used to determine
the predictive value of a number of computed image distortion metrics. It was found that mean-square-error is not a good predictor of distortion thresholds or suprathreshold perceived distortion. Some simple pointwise measures were in good agreement with psychophysical data; other more computationally intensive metrics involving spatial properties of the human visual system gave mixed results. It was determined that mean intensity, which is not accounted for in the JPEG algorithm, plays a significant role in perceived distortion.

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Experimental evaluation of psychophysical distortion metrics for JPEG-encoded images

Two experiments for evaluating psychophysical distortion metrics for JPEG-encoded images are described The first is a threshold experiment, in which subjects determined the bit rate or level of distortion at which distortion was just noticeable The second is a suprathreshold experiment in which subjects ranked image blocks according to perceived distortion The results of these experiments were used to determine the predictive value of a number of computed image distortion metrics It was found that mean-square-error is not a good predictor of distortion thresholds or suprathreshold perceived distortion Some simple point- wise measures were in good agreement with psychophysical data; other more computationally intensive metrics involving spatial properties of the human visual system gave mixed results It was determined that mean intensity, which is not accounted for in the JPEG algorithm, plays a significant role in perceived distortion© (1993) COPYRIGHT SPIE--The International Society for Optical Engineering Downloading of the abstract is permitted for personal use only

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An Integrated Model of an ALS System for Use in the High School Classroom

Traditionally, the creation of random-element stereograms is a computation-intensive process. However, by taking advantage of routines built into the Macintosh operating system, multiple-depth stereogram anaglyphs can be easily created from any black-and-white or grayscale image. Any complex shape can be selected from the pattern and offset in one eye’s view relative to the other’s to create the illusion of depth, without the need to calculate offsets on an element-by-element basis.

John A. Baro

Papers

Experimental evaluation of psychophysical distortion metrics for JPEG-encoded images

Experimental evaluation of psychophysical distortion metrics for JPEG-encoded images

An Integrated Model of an ALS System for Use in the High School Classroom

Simple algorithms for creating random-element stereograms on the macintosh