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摘要:
在量化分块压缩感知的预测编码中，低参考价值的候选者将导致较差的率失真性能。为了高效地降低编码失真，提出了一种基于螺旋逐块扫描的区域层次化预测编码方法。在以同一采样率进行观测后，各块按由内向外的扫描次序进行预测与量化。当前观测矢量从上下文感知候选集中选取与之具有最小误差的反量化矢量，作为其预测矢量；根据层次相关性，所有块被划分到3种区域之一，通过块编码模型为不同区域设定自适应的质量因子，关键区域被赋予较大的质量因子。与现有的预测编码方法相比，所提方法综合利用了矢量之间的空域相关性和层次相关性，实验结果获得了至少0.12 dB的率失真增益。
Abstract:During the predictive coding of quantized block compressive sensing, a large quantity of inefficient candidates will lead to low rate-distortion performance. To efficiently reduce the encoding distortion, this paper proposes a region-hierarchical predictive coding method for quantized block compressive sensing, which is based on the block-by-block spiral scan. After all blocks are measured at a subrate, the measurement vector of each block is numbered and encoded in spiral scan order. For the current measurement vector, its prediction vector is the inverse quantization vector with maximum similarity from its context-aware candidate set. According to its hierarchical correlation, each measurement vector is classified into one of three regions. The block coding model is used to determine adaptive quality factors for different regions, where the key region is assigned a larger quality factor. As compared with the existing predictive coding methods, the proposed method jointly utilizes the local correlation and hierarchical correlation among these vectors, and the experimental results show that at least 0.12 dB rate-distortion gain is obtained.
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图 3 逐块预测过程中的层次相关性
Figure 3. Hierarchical correlation during block-by-block measurement prediction
图 7 基于RHPC方法的Lena重构图像(使用典型的(S, Q)组合)
Figure 7. Reconstructed images of Lena by RHPC with typical (S, Q) combinations
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