Perceptual auto-regressive texture synthesis for video coding

Traditional video compression methods consider the statistical redundancy among pixels as the only adversary of compression, with the perceptual redundancy totally neglected. However, it is well-known that none criterion is as eloquent as the visual quality of an image. To reach higher compression ratios without perceptually degrading the reconstructed signal, the properties of the human visual system (HVS) need to be better exploited. Recent research indicates that HVS has different sensitivities towards different image content, based on which a novel perceptual video coding method is explored in this paper to achieve better perceptual coding quality while spending fewer bits. A new texture segmentation method exploiting just noticeable distortion (JND) profile is first devised to detect and classify texture regions in video scenes. To effectively remove temporal redundancies while preserving high visual quality, an auto-regressive (AR) model is then applied to synthesize the texture regions and combine with other regions which are encoded by the traditional hybrid coding scheme. To demonstrate the performance, the proposed scheme is integrated into the H.264/AVC video coding system. Experimental results show that on various sequences with different types of texture regions, we can reduce the bit-rate for 15% to 58% while maintaining good perceptual quality.