Compressive gradient based scalable image SoftCast
In wireless visual communication systems, it is crucial to effectively utilize channel power and bandwidth in the pursue of optimal performance, and it is worthwhile to adapt the transmission scheme to human vision system (HVS) so as to achieve perceptually appealing results. Inspired by observations that visual quality of an image is closely related to the gradient data, this paper proposes to convey visual information by random projection measurements of image gradients in an analog framework. Since HVS is more sensitive to luminance variations of image contents, which are contained in the gradient data, the proposed scheme achieves better perceptual quality than conventional analog uncoded schemes like SoftCast. Besides, the gradient transform removes the low and medium frequency components of the image hence substantially reduces the power of the signal transmitted in the analog channel, thus evidently improves the power-distortion performance of the system. Furthermore, by applying random projection to the gradients, the number of transmitted data can be adjusted according to bandwidth conditions. Another contribution of this paper is to develop an effective optimization scheme for the compressive gradient based reconstruction problem. Experimental results validate the effectiveness of the proposed transmission and reconstruction scheme under different channel signal-to-noise ratio and bandwidth conditions.