Abstract: Compared with traditional image processing methodsthe block compressive sensing can concurrently finish both acquisition and compression with a very low complexitywhich will be an ideal choice for some wireless sensors with limited power.For block compressive sensing of any imagethis paper proposes a cross subset-guided adaptive measurement method.The proposed method can adaptively allocate its sampling subrate to different regionsand also introduce the spatial prediction of mea-surement blockswhich effectively improves the quality of image reconstruction and the coding efficiency of measurement blocks.Specificallystarting from the center block in the spiral scanning orderall blocks of any image are divided into three regions:inner regionmiddle regionand outer region.Every few blocks of each region are put into a cross subset.Firstlythese blocks of each cross subset are measured by the same measurement matrix at a basic sampling subrate.Secondlyaccording to the cross-subset measurement values of three regionstheir weights are used to assign different sampling subrates for the remaining subset.Thirdlythe remaining-subset blocks of the three regions are measured by different sampling subrateswhich are proportional to their weights.For each measurement blockthe optimal predictive block is found from the surrounding area of the measurement blockand the difference between them is quantized by scalar quantization.The experimental results show that compared with theexis-ting measurement methodsthe proposed method not only improves the subjective quality of reconstructed imagebut also improves the average rate-distortion performance of image reconstruction by at least 3.2%.

Key words: Adaptive sampling subrate, Block compressive sensing, Block prediction, Cross subset, Rate distortion, Remaining subset