Motion-adapted content-based temporal scalability in very low bitrate video coding

Chung-Tao Chu, D. Anastassiou, Shih-Fu Chang
Proceedings DCC '97. Data Compression Conference  
Because of stringent bandwidth requirement, very low bitrate video coding usually uses lower frame rates in order to comply with the bitrate constraint. With a reasonably low frame rate, it can reserve basic visual information of an image sequence. However, on special occasions or for specific human understanding purposes, it can barely provide enough temporal resolution. In these cases, we would apply content-based temporal scalability to enhance temporal resolution for desired objects/areas
more » ... red objects/areas in an image, with a reasonable increase of bitrate. In this paper, we propose a motion-adapted encoding scheme for content-based temporal scalability in very low bitrat video coding. This coding scheme selectively encodes desired objects and makes proper adjustment to the rest of the scene. Content-based scalability and temporal scalability are achieved via two separate coding steps. In the first step, encoder uses object-based coding algorithm to analyse and adjust the enhancement layer images. By identifying the desired objects, the encoder makes adjustments to enhancement layer images based on relative object motions. Motion-adapted images are then coded using H.263. Because major motions are compensated before transform coding, and neither motion model description nor segmentation contributes to bitstream, both bitrate and decoder complexity are expected to be low. The object-based image analysis uses parametric motion model described in the mapping function (l), where S(X, I') is the pixel value at (X, Y) in the target image, and S'(X, Y) is the pixel value in the reference image. For the motion analysis, we use hierarchical object motion estimation* for motion estimation/segmentation. Also, block-based polygon matching (2) is used to find local translation of the observation points, where N is the window size and S is the object segmentation.
doi:10.1109/dcc.1997.582087 fatcat:qdnkblf7ubao7mzdav2jg75mji