Lambda-Domain Rate Control for Neural Image Compression

Naifu Xue, Yuan Zhang

Abstract: Rate control based on rate-distortion modeling is a classic problem in lossy image compression. Despite extensive research in neural image compression, its rate control remains understudied. In this paper, we introduce a variable rate neural image compression scheme that supports precise rate control with one-pass encoding. Our approach utilizes the Lagrangian multiplier method to transform rate control into an unconstrained optimization problem, mapping the target bitrate to λ for rate-distortion trade-off adjustment. We propose an improved exponential R-λ model and estimate the bitrates with a hybrid convolution-transformer network for model fitting. The encoder is controlled by λ, and a multi-layer modulation mechanism ensures variable rate ability. In our experiments, the proposed method outperforms the intra-frame coding of Versatile Video Coding (VVC). Meanwhile, the average rate control error is less than 5.1%, while maintaining almost identical rate-distortion performance and acceptable complexity.