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

About the Author. Acknowledgements. Acronyms. 1 Introduction. 1.1 Motivation. 1.2 Outline. 2 Vision. 2.1 Eye. 2.2 Retina. 2.3 Visual Pathways. 2.4 Sensitivity to Light. 2.5 Color Perception. 2.6 Masking and Adaptation. 2.7 Multi-channel Organization. 2.8 Summary. 3 Video Quality. 3.1 Video Coding and Compression. 3.2 Artifacts. 3.3 Visual Quality. 3.4 Quality Metrics. 3.5 Metric Evaluation. 3.6 Summary. 4 Models and Metrics. 4.1 Isotropic Contrast. 4.2 Perceptual Distortion Metric. 4.3 Summary. 5 Metric Evaluation. 5.1 Still Images. 5.2 Video. 5.3 Component Analysis. 5.4 Summary. 6 Metric Extensions. 6.1 Blocking Artifacts. 6.2 Object Segmentation. 6.3 Image Appeal. 6.4 Summary. 7 Closing Remarks. 7.1 Summary. 7.2 Perspectives. Appendix: Color Space Conversions. References. Index.

Digital Video Quality: Vision Models and Metrics

Digital video data, stored in video databases and distributed through communication networks, is subject to various kinds of distortions during acquisition, compression, processing, transmission, and reproduction. For example, lossy video compression techniques, which are almost always used to reduce the bandwidth needed to store or transmit video data, may degrade the quality during the quantization process. For another instance, the digital video bitstreams delivered over error-prone channels, such as wireless channels, may be received imperfectly due to the impairment occurred during transmission. Package-switched communication networks, such as the Internet, can cause loss or severe delay of received data packages, depending on the network conditions and the quality of services. All these transmission errors may result in distortions in the received video data. It is therefore imperative for a video service system to be able to realize and quantify the video quality degradations that occur in the system, so that it can maintain, control and possibly enhance the quality of the video data. An effective image and video quality metric is crucial for this purpose.

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41 objective video quality assessment

https://stefan.winklerbros.net/Publications/sp78.pdf

Issues in vision modeling for perceptual video quality assessment

This paper describes an application of neural networks in the field of objective measurement method designed to automatically assess the perceived quality of digital videos. This challenging issue aims to emulate human judgment and to replace very complex and time consuming subjective quality assessment. Several metrics have been proposed in literature to tackle this issue. They are based on a general framework that combines different stages, each of them addressing complex problems. The ambition of this paper is not to present a global perfect quality metric but rather to focus on an original way to use neural networks in such a framework in the context of reduced reference (RR) quality metric. Especially, we point out the interest of such a tool for combining features and pooling them in order to compute quality scores. The proposed approach solves some problems inherent to objective metrics that should predict subjective quality score obtained using the single stimulus continuous quality evaluation (SSCQE) method. This latter has been adopted by video quality expert group (VQEG) in its recently finalized reduced referenced and no reference (RRNR-TV) test plan. The originality of such approach compared to previous attempts to use neural networks for quality assessment, relies on the use of a convolutional neural network (CNN) that allows a continuous time scoring of the video. Objective features are extracted on a frame-by-frame basis on both the reference and the distorted sequences; they are derived from a perceptual-based representation and integrated along the temporal axis using a time-delay neural network (TDNN). Experiments conducted on different MPEG-2 videos, with bit rates ranging 2-6 Mb/s, show the effectiveness of the proposed approach to get a plausible model of temporal pooling from the human vision system (HVS) point of view. More specifically, a linear correlation criteria, between objective and subjective scoring, up to 0.92 has been obtained on a set of typical TV videos

/pdf/a-convolutional-neural-network-approach-for-objective-video-3e9p637gbu.pdf

A Convolutional Neural Network Approach for Objective Video Quality Assessment

An objective measurement tool for MPEG video quality

Digital image compression techniques introduce complex, time-varying distortions in coded video. There is an enormous interest in developing a reliable methodology to assess the level of distortion. The RACE project MOSAIC developed a new technique of continuous assessment (Single Stimulus Continuous Quality Evaluation, referred to as SSCQE) which has recently been incorporated in Rec. ITU-R BT.500. In this approach, viewers give a vote of quality against time according to the temporal variation in the video impairment. Under certain circumstances, it is desirable to be able to replace the human subjects with a mathematical model which produces an output approximating to the trace of continuous quality that would be obtained using the SSCQE method. In this paper, the initial stages of development of such a distortion metering tool are described. This meter takes into consideration both the psychophysical and psychological aspects of the viewers during the process of judging the quality of decoded MPEG-2 video. An overview of this new model is first discussed, followed by a brief introduction of each element in the model. Finally, some preliminary results are presented and discussed.

K. T. Tan

Papers

An objective measurement tool for MPEG video quality

Development of a perceptual distortion meter for digital video