Deformable Wiener Filter for Future Video Coding

Meng, Xuewei; Jia, Chuanmin; Zhang, Xinfeng; Wang, Shiqi; Ma, Siwei

doi:10.1109/tip.2022.3221278

Cited by 1 publication

(1 citation statement)

References 36 publications

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“…Over the past decades, a series of video coding standards have been developed, including the H.264/Advanced Video Coding (AVC) [1], H.265/High Efficiency Video Coding (HEVC) [2] and H.266/Versatile Video Coding (VVC) [3], greatly enabling a variety of applications including broadcasting, live streaming and video conferencing. Moreover, enabled by deep learning techniques, a variety of video coding tools, such as intra/inter prediction [26], [27], [28], [29], entropy coding [30], interpolation filtering [31], and inloop filtering [32], [33], [34], [35], are further enhanced for better RD performance. However, these block-based hybrid coding schemes designed for universal scenarios still lack the innate-ability in removing redundancies for face videos which typically exhibit strong prior statistical regularities.…”

Section: Hybrid Video Codingmentioning

confidence: 99%

Beyond Keypoint Coding: Temporal Evolution Inference with Compact Feature Representation for Talking Face Video Compression

Chen

Wang

et al. 2022

2022 Data Compression Conference (DCC)

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In this paper, we propose a novel framework for Interactive Face Video Coding (IFVC), which allows humans to interact with the intrinsic visual representations instead of the signals. The proposed solution enjoys several distinct advantages, including ultra-compact representation, low delay interaction, and vivid expression and headpose animation. In particular, we propose the Internal Dimension Increase (IDI) based representation, greatly enhancing the fidelity and flexibility in rendering the appearance while maintaining reasonable representation cost. By leveraging strong statistical regularities, the visual signals can be effectively projected into controllable semantics in the three dimensional space (e.g., mouth motion, eye blinking, head rotation and head translation), which are compressed and transmitted. The editable bitstream, which naturally supports the interactivity at the semantic level, can synthesize the face frames via the strong inference ability of the deep generative model. Experimental results have demonstrated the performance superiority and application prospects of our proposed IFVC scheme. In particular, the proposed scheme not only outperforms the stateof-the-art video coding standard Versatile Video Coding (VVC) and the latest generative compression schemes in terms of rate-distortion performance for face videos, but also enables the interactive coding without introducing additional manipulation processes. Furthermore, the proposed framework is expected to shed lights on the future design of the digital human communication in the metaverse. The project page can be found at https://github.com/Berlin0610/Interactive Face Video Coding.

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