Visual Attention-Driven Wireless Multicasting of Images Using Adaptive Compressed Sensing

Visual Attention-Driven Wireless Multicasting of Images Using Adaptive Compressed Sensing

Wireless multicasting of image/video signals has recently become a popular application, and various schemes have been proposed for this purpose, among them a recently-proposed scheme called SoftCast has gained a lot of attention. In SoftCast, a block-based discrete cosine transform (DCT) is applied on a given image, and the resultant coefficients are then scaled based on their expected energy within a power-distortion optimization (PDO) process. The scaled coefficients are then whitened, packetized, and transmitted over OFDM channels in an analoglike manner. Due to the linear operations used in SoftCast, each receiver is able to reconstruct the transmitted image in a graceful manner according to its channel characteristics. However, SoftCast requires a large bandwidth, and it does not consider the perceptual importance of various regions in the image. In this paper, we present a novel framework for wireless multicasting of static images. In the proposed framework, a block-wise compressed sensing (BCS) is applied on a given image to obtain measurement data. Given that due the visual attention mechanism of the brain, some parts of an image are more visually important (salient) than others, the sampling rate of various blocks is then estimated by their complexity and their visual saliency to consume the available bandwidth efficiently. The obtained data are then packetized and transmitted over OFDM channels. At the decoder side, users with different channel characteristics receive a certain number of packets, and reconstruct the transmitted image based on the available measurement data. Compared with the benchmark SoftCast scheme, the proposed framework achieves a better error resilience performance and subjective quality when some packets are lost during transmission.