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.

Working with contrast instead of luminance can facilitate numerous image processing and analysis tasks. Unfortunately, a common definition of contrast suitable for all situations does not exist. In this paper we review existing contrast definitions for natural images and propose a new isotropic contrast measure, which is computed from oriented filters. We investigate some of its properties and apply it to natural images.