Spatially Directional Predictive Coding for Block-based Compressive Sensing of Natural Images
This work addresses the need for more efficient image compression in applications like storage and transmission, though it appears incremental as it builds on existing block-based compressive sensing techniques.
The paper tackled the problem of improving rate distortion performance in block-based compressive sensing of natural images by proposing a spatially directional predictive coding (SDPC) strategy, which achieved significant improvements compared to existing methods like SQ alone and DPCM-plus-SQ.
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.