Soft Video Multicasting Using Adaptive Compressed Sensing

Abstract

Recently, soft video multicasting has gained a lot of attention, especially in broadcast and mobile scenarios where the bit rate supported by the channel may differ across receivers, and may vary quickly over time. Unlike the conventional designs that force the source to use a single bit rate according to the receiver with the worst channel quality, soft video delivery schemes transmit the video such that the video quality at each receiver is commensurate with its specific instantaneous channel quality. In this paper, we present a soft video multicasting system using an adaptive block-based compressed sensing (BCS) method. The proposed system consists of an encoder, a transmission system, and a decoder. At the encoder side, each block in each frame of the input video is adaptively sampled with a rate that depends on the texture complexity and visual saliency of the block. The obtained BCS samples are then placed into several packets, and the packets are transmitted via a channel-aware OFDM (orthogonal frequency division multiplexing) transmission system with a number of subchannels. At the decoder side, the received BCS samples are first used to build an initial approximation of the transmitted frame. To further improve the reconstruction quality, an iterative BCS reconstruction algorithm is then proposed that uses an adaptive transform and an adaptive soft-thresholding operator, which exploits the temporal similarity between adjacent frames to achieve better reconstruction quality. The extensive objective and subjective experimental results indicate the superiority of the proposed system over the state-of-the-art soft video multicasting systems.

Adaptive Block-Based Compressed Video Sensing Based on Saliency Detection and Side Information

“…Then, we compared the overall performance of the proposed scheme with that of the existing scheme. The schemes that are compared in this paper are: non-adaptive scheme (each block has the same number of measurements), adaptive scheme based on block classification [4] and adaptive scheme based on texture complexity and visual saliency [5]. (We compared the proposed sampling scheme with the adaptive sampling scheme in [4] and [5], and all the reconstruction schemes were solved by GPSR.)…”

“…The schemes that are compared in this paper are: non-adaptive scheme (each block has the same number of measurements), adaptive scheme based on block classification [ 4 ] and adaptive scheme based on texture complexity and visual saliency [ 5 ]. (We compared the proposed sampling scheme with the adaptive sampling scheme in [ 4 ] and [ 5 ], and all the reconstruction schemes were solved by GPSR.) For memory reasons, we downsampled each frame of the CIF@30 Hz format video sequences to 256 256.…”

“…This section presents the comparison of the simulation results between the proposed compressed video sensing scheme and the other schemes: non-adaptive scheme, [4,5]. (The allocation of the measurement number in [4] mainly depends on the measurement domain information, and the pixel domain information of the current frame to be sampled is not used.…”

“…In this way, the intra-frame correlation in the measurement domain and the inter-frame correlation in the pixel domain can be used to allocate the measurement number of each block at the same time. Furthermore, the proposed scheme also solves the problems that the pixel domain information of the current frame to be sampled cannot be fully utilized on the sampling side in [4]; and the adaptive sampling in [5] is directly based on pixel domain information, without considering both pixel domain information and measurement domain information.…”

“…However, for large-change blocks, the number of measurements is not obtained based on the inter-frame correlation, but based on the texture complexity of the current block itself. A block-based adaptive framework for compressed video sensing was proposed in [ 5 ] in which each block in the current frame is adaptively sampled at a rate that depends on the texture complexity and visual saliency [ 6 ] of the block. Moreover, for each frame, there will be a different total sampling rate, which is obtained according to the complexity of the frame.…”