<title>Classification of interframe difference image blocks for video compression</title>

Abstract

This paper presents classification of difference image blocks between the two successive image frames for video data compression. Difference blocks are classified to several activity categories according to the image activity distribution. The classification procedure goes in two steps: activity classification and distribution classification. In the activity classification, each interframe difference image block is classified into active or not-active class according to the amount of motion contained in the block. Distribution classification further classifies active image blocks to four activity categories, vertical, horizontal, diagonal, and uniform activities, based on the activity distribution measured by the edge feature vector in the discrete cosine transform domain. A multiplayer feedforward neural network, trained with a small set of sample classification data, successfully classified difference image blocks according to edge feature distribution. The classification scheme improves the performance of video compression at a cost of small increase in the overhead associated with the quantizer switching.