Complexity-Efficient Quantizer Selection for HEVC Encoder

The rate-distortion optimized quantization (RDOQ) used in video encoding helps to achieve high compression performance but leads to huge computation. We experimentally observe that in approximately half of the quantization blocks, RDOQ does not change the quantization results initially obtained by the conventional scalar quantizer. In this context, we design a machine learning-based quantizer selection model which lets an encoder decide whether or not to apply RDOQ process for a given transform block (TB) in advance. Our experiments show that the proposed complexity-efficient quantizer selection model reduces 9% and 35% respectively of the encoding and quantization time with BDBR loss of only 0.03%. The proposed selective quantizer achieves almost the same coding performance of RDOQ applied all the time with only around 20% of its actual usage.