Spatially Directional Predictive Coding for Block-based Compressive Sensing of Natural Images

TL;DR

This paper introduces a novel spatially directional predictive coding method for block-based compressive sensing of natural images, significantly improving rate-distortion performance by exploiting spatial correlations.

Contribution

The paper proposes SDPC, a new predictive coding strategy that utilizes directional modes for better compression in block-based compressive sensing.

Findings

SDPC-plus-SQ outperforms SQ alone in rate-distortion metrics.

Significant improvements over DPCM-plus-SQ in experimental results.

Efficient utilization of spatial correlation enhances CS reconstruction quality.

Abstract

A novel coding strategy for block-based compressive sens-ing named spatially directional predictive coding (SDPC) is proposed, which efficiently utilizes the intrinsic spatial cor-relation of natural images. At the encoder, for each block of compressive sensing (CS) measurements, the optimal pre-diction 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 quantiza-tion (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.