Distributed adaptive compressed video sensing using smoothed projected landweber reconstruction

Abstract

A novel Compressed-Sensing-based (CS-based) Distributed Video Coding (DVC) system, called Distributed Adaptive Compressed Video Sensing (DISACOS), is proposed in this paper. In this system, the input frames are divided into key frames and non-key frames, which are encoded by block CS sampling. The key frames are encoded as CS measurements at substantially higher rates than the non-key frames and decoded by the Smoothed Projected Landweber (SPL) algorithm using multi-hypothesis predictions. For the non-key frames, a small number of CS measurements are first transmitted to detect blocks having low-quality Side Information (SI) generated by the conventional interpolation or extrapolation at the decoder; then, another group of CS measurements are sampled again upon the decoder's request. To fully utilise the CS measurements, we adaptively allocate these measurements to each block in terms of different edge features. Finally, the residual frame is reconstructed using the SPL algorithm and the decoded non-key frame is simply determined as the sum of the residual frame and the SI. Experimental results have revealed that our CS-based DVC system yields better rate-distortion performance when compared with other schemes.