Learned Video Codec with Enriched Reconstruction for CLIC P-frame Coding

利用分层视觉先验重构DiT以实现极端图像压缩

Recasting DiT for Extreme Image Compression with Hierarchical Visual Priors

本文提出PrismCodec，通过渐进式适配策略将预训练DiT重构为基于比特流的单步生成解码器，在多个图像数据集上较StableCodec显著降低了LPIPS和DISTS的BD-rate，提升了极端图像压缩的率-感知性能。

Adaptation and Attention for Neural Video Coding

Neural image coding represents now the state-of-the-art image compression approach. However, a lot of work is still to be done in the video domain. In this work, we propose an end-to-end learned video codec that introduces several architectural novelties as well as training novelties, revolving around the concepts of adaptation and attention. Our codec is organized as an intra-frame codec paired with an inter-frame codec. As one architectural novelty, we propose to train the inter-frame codec model to adapt the motion estimation process based on the resolution of the input video. A second arch…

Optical Flow and Mode Selection for Learning-based Video Coding

This paper introduces a new method for inter-frame coding based on two complementary autoencoders: MOFNet and CodecNet. MOFNet aims at computing and conveying the Optical Flow and a pixel-wise coding Mode selection. The optical flow is used to perform a prediction of the frame to code. The coding mode selection enables competition between direct copy of the prediction or transmission through CodecNet. The proposed coding scheme is assessed under the Challenge on Learned Image Compression 2020 (CLIC20) P-frame coding conditions, where it is shown to perform on par with the state-of-the-art vide…

多尺度图像表示压缩

Multi-scale Image Representation Compression

本文提出基于NVRC端到端压缩流程的过拟合图像编解码器MIRC，引入多尺度表示与跨阶段参数共享，在CLIC2020验证集上较VVC实现10.5% BD-rate节省，且解码预算可按需选择。

从2D到3D的一键升级：面向立体视频会议的夹心式RGB-D视频压缩

One-Click Upgrade from 2D to 3D: Sandwiched RGB-D Video Compression for Stereoscopic Teleconferencing

针对立体视频会议中现有2D编解码器不适配立体RGB-D视频、3D扩展方案复杂的问题，提出用神经前后处理器封装2D编解码器的方法，结合几何感知损失，在相同渲染质量下比传统方案及MV-HEVC省30%比特率，无需专用硬件升级。

双约束扩散图像压缩用于操作率失真感知优化

Dual-Constrained Diffusion Image Compression for Operational Rate-Distortion-Perception Optimization

提出DCIC框架，结合学习编解码器和基于扩散的解码器，通过联合失真和等幂约束实现率失真感知帕累托前沿的连续导航，无需额外码率开销。