提出块级速率控制新方法，提速百倍且精度超98%。
Accelerating block-level rate control for learned image compression
- 基于专用于学习图像压缩的D-λ模型实现块级速率控制。
- 引入块间相关性预测，速度提升100倍，精度保持98%以上。
- 适合需要高效高保真图像压缩的应用场景。
尽管深度学习图像压缩（LIC）实现了前所未有的压缩效率，但现有方法通常通过调整单一质量因子来近似目标码率，可能影响速率控制效果。考虑到不同空间内容的率失真（R-D）特性，本文提出一种基于新型D-λ模型的块级速率控制方法。进一步地，利用块间相关性，设计了一种块级率失真预测算法，大幅加速块级速率控制的同时仍保证高精度。实验结果表明，所提方法在保持超过98%精度的前提下，实现最高达100倍的速度提升。该方法为每个块提供最优比特分配，从而显著提升整体压缩性能，对块级LIC具有重要应用潜力。
原文摘要 · Abstract (English)
Despite the unprecedented compression efficiency achieved by deep learned image compression (LIC), existing methods usually approximate the desired bitrate by adjusting a single quality factor for a given input image, which may compromise the rate control results. Considering the Rate-Distortion (R - D) characteristics of different spatial content, this work introduces the block-level rate control based on a novel D - λ model specific for LIC. Furthermore, we try to exploit the inter-block correlations and propose a block-wise R - D prediction algorithm which greatly speeds up block-level rate control while still guaranteeing high accuracy. Experimental results show that the proposed rate control achieves up to 100 times, speed-up with more than 98% accuracy. Our approach provides an optimal bit allocation for each block and therefore improves the overall compression performance, which offers great potential for block-level LIC.
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