NLIC: Non-Uniform Quantization-Based Learned Image Compression

In recent years, Learned Image Compression (LIC) has undergone rapid evolution. However, it is worthy noting that most prevalent LIC methodologies still rely on uniform Scalar Quantization (SQ) for latent features. This overlooks the untapped potential of contextual information, which could be leveraged to significantly reduce statistical redundancies. Prior researches have explored Vector Quantization (VQ)'s adaptability to diverse data distributions, yet it introduces significant computational complexity into LIC, hindering its practical implementation. Consequently, in this work, we propose the Contextual Sequential Quantization (CSQ) method, which progressively discretizes the latent features of LIC by harnessing content contextual information and image textural priors. Our proposed CSQ signifies progress in LIC by blending the computational efficiency of SQ with a substantial approach towards the adaptability of VQ. We further propose the Center Compensation Module (CCM) based on the proposed CSQ. This module strategically determines adaptive quantization centers, leading to a direct enhancement of reconstruction quality without compromising the bit-rate. Moreover, it is worth noticing that existing LIC approaches face challenges in leveraging hyper side information to effectively enhance transformations, which is attributed to the entanglement of the hyperprior generation module with the main transformations. Consequently, we propose to decouple the hyperprior module from main transformations, and design the Hyperprior-Assisted Transformation (HAT) unit to feed hyperprior back into main transformations. This further improves the coding performance. By integrating all together the proposed CSQ, CCM, and HAT, our proposed Non-uniform quantization-based LIC (NLIC) method attains state-of-the-art rate-distortion (R-D) performance among existing LIC methodologies.