Block-Level Rate Control for Learnt Image Coding

Xining Wang, Ming Lu, Zhan Ma

Abstract: Learnt image coding (LIC) methods recently offered state-of-the-art efficiency by training separate models for individual bitrate which apparently was impractical. Variable-rate coding with a single or very few LIC models was emerged and mostly implemented to process a whole image directly (e.g., a single control rate-distortion factor $\lambda$ for a given image to approach target rate). This work provides a novel block-level rate control by applying the UnEqual Rate Allocation (UERA) to nonoverlapped image blocks, which basically exploits the spatial heterogeneousness of the underlying content. Such block-level UERA is enabled by modeling the rate-distortion (R-D) function of each block, by which we optimize block-wise $\lambda$s to maximize the overall R-D performance. Experiments show that our method can accurately adapt a wide range of bitrates by a single model, and provide almost identical performance as the solutions using multiple rate-specific models. Additionally, such block-level LIC significantly reduces the consumption of peak running memory and computational complexity, which is attractive for practical implementations.