Rate-distortion optimized block classification and bit allocation in screen video compression
Due to the divergent characteristics of image contents and text contents in screen videos, how to make the joint optimization leveraging rate-distortion (R-D) optimized block classification and bit allocation is critical to the compression performance. In this paper, a general model-based solution is proposed as an attempt to solve this problem. The contributions of this paper are twofold: First, the rate and distortion characteristics of image blocks and text blocks in block-based content-adaptive screen video encoder (BASC) are carefully studied, and the rate and distortion models are proposed. Second, with the proposed rate and distortion models, the R-D optimized block classification and bit allocation are derived using bisection searched Lagrange multiplier method. Experimental results demonstrate that the proposed R-D optimized block classification and bit allocation algorithms are able to adapt to diverse screen contents, which results in a significant gain of up to 4.5dB in PSNR.