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...

This paper attempts to present a survey of quantitative quality/impairment metrics based on human visual sensitivity, and to describe a couple of quantitative metrics recently developed and reported for digital image and video coding.