  In High Efficiency Video Coding (HEVC), excellent rate-distortion (RD)
performance is achieved in part by having a flexible quadtree coding unit (CU)
partition and a large number of intra-prediction modes. Such an excellent RD
performance is achieved at the expense of much higher computational complexity.
In this paper, we propose a learned fast HEVC intra coding (LFHI) framework
taking into account the comprehensive factors of fast intra coding to reach an
improved configurable tradeoff between coding performance and computational
complexity. First, we design a low-complex shallow asymmetric-kernel CNN
(AK-CNN) to efficiently extract the local directional texture features of each
block for both fast CU partition and fast intra-mode decision. Second, we
introduce the concept of the minimum number of RDO candidates (MNRC) into fast
mode decision, which utilizes AK-CNN to predict the minimum number of best
candidates for RDO calculation to further reduce the computation of intra-mode
selection. Third, an evolution optimized threshold decision (EOTD) scheme is
designed to achieve configurable complexity-efficiency tradeoffs. Finally, we
propose an interpolation-based prediction scheme that allows for our framework
to be generalized to all quantization parameters (QPs) without the need for
training the network on each QP. The experimental results demonstrate that the
LFHI framework has a high degree of parallelism and achieves a much better
complexity-efficiency tradeoff, achieving up to 75.2% intra-mode encoding
complexity reduction with negligible rate-distortion performance degradation,
superior to the existing fast intra-coding schemes.
