Deep Joint Source-Channel Coding for Wireless Video Transmission with Asymmetric Context

TL;DR

This paper introduces a deep joint source-channel coding method for wireless video transmission that leverages asymmetric context and feature propagation to improve performance and reduce error accumulation.

Contribution

The paper proposes a novel neural network framework that learns encoding and decoding conditions from asymmetric contexts without requiring a transmission pipeline, enhancing video transmission efficiency.

Findings

Outperforms existing deep video transmission methods in performance.

Effectively mitigates error accumulation in wireless video transmission.

Enables variable bandwidth transmission with content-adaptive coding.

Abstract

In this paper, we propose a high-efficiency deep joint source-channel coding (JSCC) method for video transmission based on conditional coding with asymmetric context. The conditional coding-based neural video compression requires to predict the encoding and decoding conditions from the same context which includes the same reconstructed frames. However in JSCC schemes which fall into pseudo-analog transmission, the encoder cannot infer the same reconstructed frames as the decoder even a pipeline of the simulated transmission is constructed at the encoder. In the proposed method, without such a pipeline, we guide and design neural networks to learn encoding and decoding conditions from asymmetric contexts. Additionally, we introduce feature propagation, which allows intermediate features to be independently propagated at the encoder and decoder and help to generate conditions, enabling the framework to greatly leverage temporal correlation while mitigating the problem of error accumulation. To further exploit the performance of the proposed transmission framework, we implement content-adaptive coding which achieves variable bandwidth transmission using entropy models and masking mechanisms. Experimental results demonstrate that our method outperforms existing deep video transmission frameworks in terms of performance and effectively mitigates the error accumulation. By mitigating the error accumulation, our schemes can reduce the frequency of inserting intra-frame coding modes, further enhancing performance.