Video Coding for Machines: Partial transmission of SIFT features

Bandwidth-Agile Image Transmission with Deep Joint Source-Channel Coding

We propose deep learning based communication methods for adaptive-bandwidth transmission of images over wireless channels. We consider the scenario in which images are transmitted progressively in layers over time or frequency, and such layers can be aggregated by receivers in order to increase the quality of their reconstructions. We investigate two scenarios, one in which the layers are sent sequentially, and incrementally contribute to the refinement of a reconstruction, and another in which the layers are independent and can be retrieved in any order. Those scenarios correspond to the well…

Deep Joint Source-Channel Coding for Wireless Image Transmission with Adaptive Rate Control

We present a novel adaptive deep joint source-channel coding (JSCC) scheme for wireless image transmission. The proposed scheme supports multiple rates using a single deep neural network (DNN) model and learns to dynamically control the rate based on the channel condition and image contents. Specifically, a policy network is introduced to exploit the tradeoff space between the rate and signal quality. To train the policy network, the Gumbel-Softmax trick is adopted to make the policy network differentiable and hence the whole JSCC scheme can be trained end-to-end. To the best of our knowledge,…

使用HEVC的面向实时应用的多描述视频编码

Multiple description video coding for real-time applications using HEVC

本文提出一种兼容HEVC的低复杂度、低延迟双信道多描述编码（MDC）方案，通过自适应IDR帧周期提升视频流在无线传输中的抗丢包鲁棒性。

DeepJSCC-f: Deep Joint Source-Channel Coding of Images with Feedback

We consider wireless transmission of images in the presence of channel output feedback. From a Shannon theoretic perspective feedback does not improve the asymptotic end-to-end performance, and separate source coding followed by capacity-achieving channel coding, which ignores the feedback signal, achieves the optimal performance. It is well known that separation is not optimal in the practical finite blocklength regime; however, there are no known practical joint source-channel coding (JSCC) schemes that can exploit the feedback signal and surpass the performance of separation-based schemes. …

多址信道下的分布式深度联合信源信道编码

Distributed Deep Joint Source-Channel Coding over a Multiple Access Channel

针对有限分组长度下分离式信源信道编码次优的问题，提出非正交分布式深度联合信源信道编码图像传输方案，实验证明其在低带宽比下重建图像质量显著优于正交传输方案，并公开了源代码。

Generative Joint Source-Channel Coding for Semantic Image Transmission

Recent works have shown that joint source-channel coding (JSCC) schemes using deep neural networks (DNNs), called DeepJSCC, provide promising results in wireless image transmission. However, these methods mostly focus on the distortion of the reconstructed signals with respect to the input image, rather than their perception by humans. However, focusing on traditional distortion metrics alone does not necessarily result in high perceptual quality, especially in extreme physical conditions, such as very low bandwidth compression ratio (BCR) and low signal-to-noise ratio (SNR) regimes. In this w…