Image Compression Based On Motion Segmentation

摘要

Humans subjectively evaluate the content of a scene. For content-based indexing and retrieval, we index and retrieve the scene containing moving objects because they remain in our memory longer than static scene. The importance of processing moving objects has been demonstrated in image compression, content-based data processing, and a variety of video processing techniques. This paper proposes the method of segmenting and then compressing the image including moving objects. The image scene is usually composed of the motion region (MR) and static region (SR). For simplicity, the camera motion region is assigned to the SR, because the region has similar characteristics with the SR. The MR is extracted by our segmentation technique. We propose a line scan-based segmentation method being composed of motion estimation and label assignment. The dominant region owning the largest number of blocks with the same label is classified as SR. The region excluding the SR is MR. Then, the MR is processed by lossless compression or lossy with low-compression ratio to preserve the high quality, and the SR by a lossy method with high compression ratio. Rather than applying separate methods to MR and SR, we use a hybrid compression method based on DCT (Discrete Cosine Transform). Experiments on test video clips show the increase of the compression ratio with respect to the lossless com-pression and better visualization of the moving objects compared with the lossy compression.