Channel-adaptive wireless image transmission with OFDM

We present a learning-based channel-adaptive joint source and channel coding (CA-JSCC) scheme for wireless image transmission over multipath fading channels. The proposed method is an end-to-end autoencoder architecture with a dual-attention mechanism employing orthogonal frequency division multiplexing (OFDM) transmission. Unlike the previous works, our approach is adaptive to channel-gain and noise-power variations by exploiting the estimated channel state information (CSI). Specifically, with the proposed dual-attention mechanism, our model can learn to map the features and allocate transmission-power resources judiciously to the available subchannels based on the estimated CSI. Extensive numerical experiments verify that CA-JSCC achieves state-of-the-art performance among existing JSCC schemes. In addition, CA-JSCC is robust to varying channel conditions and can better exploit the limited channel resources by transmitting critical features over better subchannels

Scalable video/image transmission using rate compatible PUM turbo codes

The robust delivery of video over emerging wireless networks poses many challenges due to the heterogeneity of access networks, the variations in streaming devices, and the expected variations in network conditions caused by interference and coexistence. The proposed approach exploits the joint optimization of a wavelet-based scalable video/image coding framework and a forward error correction method based on PUM turbo codes. The scheme minimizes the reconstructed image/video distortion at the decoder subject to a constraint on the overall transmission bitrate budget. The minimization is achieved by exploiting the rate optimization technique and the statistics of the transmission channel

Enabling error-resilient internet broadcasting using motion compensated spatial partitioning and packet FEC for the dirac video codec

Video transmission over the wireless or wired network require protection from channel errors since compressed video bitstreams are very sensitive to transmission errors because of the use of predictive coding and variable length coding. In this paper, a simple, low complexity and patent free error-resilient coding is proposed. It is based upon the idea of using spatial partitioning on the motion compensated residual frame without employing the transform coefficient coding. The proposed scheme is intended for open source Dirac video codec in order to enable the codec to be used for Internet broadcasting. By partitioning the wavelet transform coefficients of the motion compensated residual frame into groups and independently processing each group using arithmetic coding and Forward Error Correction (FEC), robustness to transmission errors over the packet erasure wired network could be achieved. Using the Rate Compatibles Punctured Code (RCPC) and Turbo Code (TC) as the FEC, the proposed technique provides gracefully decreasing perceptual quality over packet loss rates up to 30%. The PSNR performance is much better when compared with the conventional data partitioning only methods. Simulation results show that the use of multiple partitioning of wavelet coefficient in Dirac can achieve up to 8 dB PSNR gain over its existing un-partitioned method

Source-Channel Diversity for Parallel Channels

We consider transmitting a source across a pair of independent, non-ergodic channels with random states (e.g., slow fading channels) so as to minimize the average distortion. The general problem is unsolved. Hence, we focus on comparing two commonly used source and channel encoding systems which correspond to exploiting diversity either at the physical layer through parallel channel coding or at the application layer through multiple description source coding. For on-off channel models, source coding diversity offers better performance. For channels with a continuous range of reception quality, we show the reverse is true. Specifically, we introduce a new figure of merit called the distortion exponent which measures how fast the average distortion decays with SNR. For continuous-state models such as additive white Gaussian noise channels with multiplicative Rayleigh fading, optimal channel coding diversity at the physical layer is more efficient than source coding diversity at the application layer in that the former achieves a better distortion exponent. Finally, we consider a third decoding architecture: multiple description encoding with a joint source-channel decoding. We show that this architecture achieves the same distortion exponent as systems with optimal channel coding diversity for continuous-state channels, and maintains the the advantages of multiple description systems for on-off channels. Thus, the multiple description system with joint decoding achieves the best performance, from among the three architectures considered, on both continuous-state and on-off channels.Comment: 48 pages, 14 figure