Image coding and image activity measurement

In this paper, a novel image analysis technique is proposed, which may be performed prior to coding in order to decide what is the most significant information to encode. In the proposed system, the image to be coded is first partitioned into a large number of sub-blocks of N*N pixels. The blocks can then be stored into two major classes according to the level of the visual activity present. The classification is based on analyzing the local histogram within each sub-block. In this paper, we initially analyze the image blocks to separate uniform blocks from those that can be classified as non-uniform blocks. Adjacent uniform blocks with the same statistics are merged to form large blocks. These blocks can then be coded by their mean values. It is also shown that the non-uniform blocks may also be classified into three categories with different levels of activity.