Spatially directional predictive coding for block-based compressive sensing of natural images

Jian Zhang, Debin Zhao, Feng Jiang

Abstract: A novel coding strategy for block-based compressive sensing named spatially directional predictive coding (SDPC) is proposed, which efficiently utilizes the intrinsic spatial correlation of natural images. At the encoder, for each block of compressive sensing (CS) measurements, the optimal prediction is selected from a set of prediction candidates that are generated by four designed directional predictive modes. Then, the resulting residual is processed by scalar quantization (SQ). At the decoder, the same prediction is added onto the de-quantized residuals to produce the quantized CS measurements, which is exploited for CS reconstruction. Experimental results substantiate significant improvements achieved by SDPC-plus-SQ in rate distortion performance as compared with SQ alone and DPCM-plus-SQ.