Conditional Latent Coding with Learnable Synthesized Reference for Deep Image Compression

TL;DR

This paper introduces a novel conditional latent coding method for deep image compression that synthesizes dynamic references from an external dictionary, significantly improving compression efficiency and robustness.

Contribution

The paper proposes a new end-to-end framework for conditional latent coding using a synthesized reference from a universal feature dictionary, enhancing image compression performance.

Findings

Improves compression performance by up to 1.2 dB.

Achieves robustness with error bounds scaling logarithmically with dictionary size.

Maintains small overhead of about 0.5% bits per pixel.

Abstract

In this paper, we study how to synthesize a dynamic reference from an external dictionary to perform conditional coding of the input image in the latent domain and how to learn the conditional latent synthesis and coding modules in an end-to-end manner. Our approach begins by constructing a universal image feature dictionary using a multi-stage approach involving modified spatial pyramid pooling, dimension reduction, and multi-scale feature clustering. For each input image, we learn to synthesize a conditioning latent by selecting and synthesizing relevant features from the dictionary, which significantly enhances the model's capability in capturing and exploring image source correlation. This conditional latent synthesis involves a correlation-based feature matching and alignment strategy, comprising a Conditional Latent Matching (CLM) module and a Conditional Latent Synthesis (CLS) module. The synthesized latent is then used to guide the encoding process, allowing for more efficient compression by exploiting the correlation between the input image and the reference dictionary. According to our theoretical analysis, the proposed conditional latent coding (CLC) method is robust to perturbations in the external dictionary samples and the selected conditioning latent, with an error bound that scales logarithmically with the dictionary size, ensuring stability even with large and diverse dictionaries. Experimental results on benchmark datasets show that our new method improves the coding performance by a large margin (up to 1.2 dB) with a very small overhead of approximately 0.5\% bits per pixel. Our code is publicly available at https://github.com/ydchen0806/CLC.