In this paper, a new image database, TID2008, for evaluation of full-reference visual quality assessment metrics is described. It contains 1700 test images (25 reference images, 17 types of distortions for each reference image, 4 different levels of each type of distortion). Mean Opinion Scores (MOS) for this database have been obtained as a result of more than 800 experiments. During these tests, observers from three countries (Finland, Italy, and Ukraine) have carried out about 256000 individual human quality judgments. The obtained MOS can be used for effective testing of different visual quality metrics as well as for the design of new metrics. Using the designed image database, we have tested several known quality metrics. The designed test image database is freely available for downloading and utilization in scientific investigations.

TID2008 – A database for evaluation of full-reference visual quality assessment metrics

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

Perceptual visual quality metrics: A survey

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.

/pdf/motion-tuned-spatio-temporal-quality-assessment-of-natural-yyj5f6tarw.pdf

Motion Tuned Spatio-Temporal Quality Assessment of Natural Videos

Measurement of image or video quality is crucial for many image-processing algorithms, such as acquisition, compression, restoration, enhancement, and reproduction. Traditionally, image quality assessment (QA) algorithms interpret image quality as similarity with a "reference" or "perfect" image. The obvious limitation of this approach is that the reference image or video may not be available to the QA algorithm. The field of blind, or no-reference, QA, in which image quality is predicted without the reference image or video, has been largely unexplored, with algorithms focussing mostly on measuring the blocking artifacts. Emerging image and video compression technologies can avoid the dreaded blocking artifact by using various mechanisms, but they introduce other types of distortions, specifically blurring and ringing. In this paper, we propose to use natural scene statistics (NSS) to blindly measure the quality of images compressed by JPEG2000 (or any other wavelet based) image coder. We claim that natural scenes contain nonlinear dependencies that are disturbed by the compression process, and that this disturbance can be quantified and related to human perceptions of quality. We train and test our algorithm with data from human subjects, and show that reasonably comprehensive NSS models can help us in making blind, but accurate, predictions of quality. Our algorithm performs close to the limit imposed on useful prediction by the variability between human subjects.

/pdf/no-reference-quality-assessment-using-natural-scene-10wc0nl12q.pdf

No-reference quality assessment using natural scene statistics: JPEG2000

A survey of hybrid MC/DPCM/DCT video coding distortions

This paper presents a performance comparison of different loop filtering techniques in a generic hybrid video coding algorithm. This study will compare the performance of the filtering techniques by integrating each of the loop filters separately into a MPEG-1 compliant codec, and coding a number of video sequences at various bit-rates and motion compensation (MC) accuracies. The performance of the filters will be assessed in terms of the energy of the displaced frame difference. Comparisons are conducted between four filtering techniques: (1) the 1:2:1 loop filter described in ITU Recommendation H.261; (2) an MC-accuracy dependent 3-tap filter, whose tap weights are based upon a first-order Markov model of the source; (3) a spatially-adaptive filter for the blocking-effect based on the theory of Projections Onto Convex Sets (POCS); and (4) an anisotropic filter for the reduction of the blocking-effect. Our results will examine the effect that traditional low-pass loop filters have on MC prediction quality, and compare this to a POCS-based loop filter. The filtering of only the blocking-effect will also provide an indication of the contribution that the blocking-effect has on the overall high- frequency distortions that are reduced by the low-pass loop filters.© (1996) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Michael Yuen

Papers

A survey of hybrid MC/DPCM/DCT video coding distortions

Performance comparison of loop filtering in generic MC/DPCM/DCT video coding