Image analysis for video artifact estimation and measurement

The ultimate goal of vide compression is to maximize picture quality while minimizing bandwidth requirement. In most video storage and communication applications, the constraints are often expressed as limitation in delivery bandwidth or storage capacity. The objective of video encoder development is, therefore, to minimize the amount of distortions introduced by video compression and transmission. As an enabling technology, measurement of compression distortions and quality impact to end-users is crucial to video encoder optimization. While the field is developing quickly, there have been two different paradigms for video quality measurement that are being studied, picture quality models that use reference pictures, and the models that do not. An issue common to picture quality measurement in both paradigms is to obtain accurate measurement of picture distortions. In this paper, we review the requirements of these two measurement paradigms and propose two image analysis methods that address some specific issues of picture distortion measurement. First we describe a fast video alignment approach necessary for picture distortion measurement models that require references. In the rest of the paper, we propose a blur estimation scheme to measure blurring degradation introduced by video compression and imaging systems. We will then review its reference-free distortion measurement performance using data from two experiments.