Region-hierarchical predictive coding for quantized block compressive sensing

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.