Segmentation-Based Video Compression Using Texture and Motion Models

In recent years, there has been a growing interest in developing novel techniques for increasing the coding efficiency of video compression methods. One approach is to use texture and motion models of the content in a scene. Based on these models parts of the video frame are not coded or “skipped” by a classical motion compensated coder. The models are then used at the decoder to reconstruct the missing or skipped regions. In this paper, we describe several spatial-texture models for video coding. We investigate several texture features in combination with two segmentation strategies in order to detect texture regions in a video sequence. These detected areas are not encoded using motion compensated coding. The model parameters are sent to the decoder as side information. After the decoding process, frame reconstruction is done by inserting the skipped texture areas into the decoded frames. Using similar approach, we consider motion models based on human visual motion perception. We describe a motion classification model to separate foreground objects containing noticeable motion from the background. This motion model is then used in the encoder to again allow regions to be skipped and not coded using a motion compensated encoder. Our results indicate significant increase in terms of coding efficiency in comparison to the spatial texture-based methods. Finally, we discuss the effects and tradeoffs of these techniques based on perceptual experiments and show that in many cases the coding efficiency can be increased by up to 25% given a fixed perceptual quality.