Generative Latent Coding for Ultra-Low Bitrate Image and Video Compression

TL;DR

This paper introduces GLC, a novel generative latent coding approach for ultra-low bitrate image and video compression that leverages latent space transform coding to improve perceptual quality and efficiency.

Contribution

The paper proposes GLC models using VQ-VAE latent space for better perceptual alignment, with enhancements like hyper modules and semantic loss, achieving state-of-the-art ultra-low bitrate compression.

Findings

GLC-image achieves <0.04 bpp with high FID

GLC-video saves 65.3% bitrate over PLVC

Enhanced hyper modules improve compression quality

Abstract

Most existing approaches for image and video compression perform transform coding in the pixel space to reduce redundancy. However, due to the misalignment between the pixel-space distortion and human perception, such schemes often face the difficulties in achieving both high-realism and high-fidelity at ultra-low bitrate. To solve this problem, we propose \textbf{G}enerative \textbf{L}atent \textbf{C}oding (\textbf{GLC}) models for image and video compression, termed GLC-image and GLC-Video. The transform coding of GLC is conducted in the latent space of a generative vector-quantized variational auto-encoder (VQ-VAE). Compared to the pixel-space, such a latent space offers greater sparsity, richer semantics and better alignment with human perception, and show its advantages in achieving high-realism and high-fidelity compression. To further enhance performance, we improve the hyper prior by introducing a spatial categorical hyper module in GLC-image and a spatio-temporal categorical hyper module in GLC-video. Additionally, the code-prediction-based loss function is proposed to enhance the semantic consistency. Experiments demonstrate that our scheme shows high visual quality at ultra-low bitrate for both image and video compression. For image compression, GLC-image achieves an impressive bitrate of less than $0.04$ bpp, achieving the same FID as previous SOTA model MS-ILLM while using $45\%$ fewer bitrate on the CLIC 2020 test set. For video compression, GLC-video achieves 65.3\% bitrate saving over PLVC in terms of DISTS.