Complexity reduction of joint temporal-spatial bit allocation using R-D models for video streaming
The joint temporal-spatial bit allocation problem was examined in our previous work to achieve proper temporal-spatial quality tradeoff for low bit rate video coding. Both theoretical analysis and performance benchmark of several optimal/suboptimal algorithms were presented. However, the high computational complexities of these solutions prevent them from being used in practical applications. To solve this problem, rate-distortion (R-D) models are adopted in this work to reduce the complexity of the proposed rate control scheme. Different R-D models are applied to intra, inter and skipped frames, respectively, with consideration of both prediction and interpolation dependencies among successive frames. A look ahead window (LAW) structure is implemented, with buffer and delay constraints applied, for continuous and smooth video playback. The approximation errors of the proposed R-D models are very small, which results in small degradation of the compressed video quality, while the complexity of rate control is greatly reduced so that it is suitable for streaming applications. It is shown by experiments that the proposed rate control method can achieve better overall quality than the standard MPEG-4/TM7 rate control.