Learning-Based Efficient Quantizer Selection for Fast HEVC Encoder

Abstract

The rate-distortion optimized quantization (RDOQ) in HEVC has improved the coding efficiency of the conventional uniform scalar quantization (SQ) very much. Since the RDOQ is computationally complex, in this paper, we investigate a way of performing RDOQ more efficiently in HEVC. Based on our statistical observation of non-trivial percentage of transform blocks (TB) for which RDOQ does not change their quantization results of SQ, we design a learning-based quantizer selection scheme which can tell in advance whether RDOQ is expected to modify the quantization levels calculated by SQ. Only those TBs likely to be changed by RDOQ are subject to the actual RDOQ process. For the remaining TBs, we design an improved SQ which adapts the dead-zone interval size and round offset based on coefficient group and entropy coding features. The proposed improved SQ has much lower computational complexity than RDOQ while achieving better coding efficiency than the conventional SQ. The experimental results show that our efficient quantization scheme respectively provides 9% and 34% of encoding and quantization time reduction by selectively performing RDOQ only for 21% of TBs. The average BDBR performances of Y, Cb, and Cr channels are respectively–0.03%, 0.48%, and 0.45%.