JVT-G012算法存在两个不足：一是当存在快速运动或场景变换时，线性MAD（平均绝对偏差）预测法的效果变得很差；二是H.264存在更多的运动补偿模式，更多的比特用于非纹理数据的编码，而G012算法简单地采用已编码帧的非纹理数据来预测当前帧的非纹理数据，这种方法已不再有效。本文给出了一种基于宏块的差图像直方图法，并根据图像复杂度在编码单元之间合理分配比较率。同时提出了一种时空结合的MAD预测模型和一种更加灵活的非纹理数据的预测方法。实验结果表明，该方法能更加接近目标码率，并获得更高的视觉质量和PSNR。

There are two limitations in JVT-G012 scheme: firstly, the linear MAD prediction model performs poorly due to high motion or scene changes. Secondly, there exist much more complex motion compensation strategies in H.264, and a higher percentage of bits is required to encode non-texture data. Thus the method that simply predicts non-texture bits through the previous coded frames is no longer efficient. First of all, a macroblock-based histogram of difference is introduced, and bits are allocated rationally among coding units according to image complexities. Furthermore, a novel rate control scheme named Spatio-Temporal MAD prediction model is presented, and a more flexible non-texture prediction scheme is derived. As shown in experiments, the proposed algorithm achieves a bit rate closer to the target, and provides an improved visual quality and the higher PSNR.