In this paper I present a distortion metric for color video sequences. It is based on a contrast gain control model of the human visual system that incorporates spatial and temporal aspects of vision as well as color perception. The model achieves a close fit to contrast sensitivity and contrast masking data from several different psychophysical experiments for both luminance and color stimuli. The metric is used to assess the quality of MPEG-coded sequences.

Lossy compression algorithms used in digital video systems produce artifacts whose visibility strongly depends on the actual image content. Simple error measures such as RMSE or PSNR, albeit popular, ignore this important fact and are only a mediocre predictor of perceived quality. Many applications require more reliable assessment methods. This paper discusses issues in vision modeling for perceptual video quality assessment (PVQA). Its purpose is not to describe a particular model or system, but rather to summarize and to provide pointers to up-to-date knowledge of important characteristics of the human visual system, to explain how these characteristics may be incorporated in vision models for PVQA, to give a brief overview of the state-of-the-art and current efforts in this field, and to outline directions for future research.

The design of reliable visual quality metrics is complicated by our limited knowledge of the human visual system and the resulting variety of pertinent vision models. We have begun to analyze and compare a number of implementation choices for some components found in most of today's visual quality metrics that are based on a model of human vision and present the first results here. Keywords: Vision model evaluation, video quality assessment, perceptual distortion metric 1. INTRODUCTION The introduction of digital TV systems on the consumer market has lead to a rising demand for pertinent measurement tools, not only on the bitstream and network level, but also for video quality assessment on a perceptual level. Consequently, considerable effort has been put in the development of visual quality metrics in recent years, many of which rely on models of the human visual system (an overview of current modeling approaches was presented elsewhere by the author 14 ). As a matter of fact, t...

Much of the work on visual quality assessment has been devoted to gray-level images; metrics taking into account color information and the temporal component are still relatively rare. This paper presents a quality metric for color video which is based on a recent contrast gain control model of the human visual system. It is used to assess the quality of MPEG-coded sequences and exhibits a behavior that is consistent with subjective ratings.