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...

Traditional visual quality metrics measure fidelity instead of quality, even though fidelity, i.e. the accuracy of the reproduction of the original on the display, is just one of the many factors determining the overall perceived quality. In this paper, the addition of image appeal attributes is investigated in order to bridge this gap. Sharpness and colorfulness are identified among these attributes and are quantified by means of an isotropic local contrast measure and the distribution of chroma, respectively. The benefits of using these attributes are demonstrated with the help of data from subjective experiments. Keywords: Image appeal, vision modeling, video quality assessment, perceptual distortion metric 1.