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.

We propose a new model for the prediction of distortion visibility in digital image sequences, which is aimed at use in digital video compression algorithms. The model is an extension of our spatial vision model with a spatio-temporal contrast sensitivity function and an eye movement estimation algorithm. Due to the importance of smooth pursuit eye movements when viewing image sequences, eye movements cannot be neglected in a spatio-temporal vision model. Although eye movements can be incorporated by motion compensation of the contrast sensitivity function, the requirements for this motion compensation are different than those for motion compensated prediction in video coding. We propose an algorithm for the estimation of smooth pursuit eye movements, under the worst-case assumption that the observer is capable of tracking all objects in the image. In image and image sequence compression, models which predict the visibility of coding distortions can be used to improve the visual quality of the total compression system. Many models have been proposed for the processing of spatial information in the human visual system to predict distortion visibility in coded images [1-5]. However, the number

We propose a vision model for predicting the significance of coding errors in digital image sequences. Eye movements have a significant effect on distortion visibility, and hence they form an important part of the model. An algorithm is proposed to make a prediction of the eye movements a viewer will make when watching the image sequence. Furthermore, the spatio-temporal frequency sensitivity and masking effects are included in the model. The model is based on a decomposition of the signal into frequency and orientation bands, in order to allow accurate modeling of spatial masking, which occurs mainly between signal components which are similar in position, orientation, and frequency. 1. Introduction The Mean Squared Error (MSE) is widely used as a distortion measure for optimization of image compression algorithms, probably because its calculation is very simple. However, it is not a very good measure of quality as experienced by human viewers. Therefore, many alternative distortion ...