For omnidirectional videos (ODVs), the existing off-line coding approaches are designed based on the spatial or perceptual distortion in a whole ODV frame, ignoring the fact that subjects can only access viewports. To improve the subjective quality inside the viewports, this paper proposes an off-line viewport-adaptive rate control (RC) approach for ODVs in high efficiency video coding (HEVC) framework. Specifically, we predict the viewport candidates with importance weights and develop a viewport saliency detection model. Then, the predicted candidates and detected saliency are taken into account in our viewport-adaptive CTU traversal and bit allocation scheme. Finally, the experimental results validate that our approach is effective in saving bit-rates and improving subjective quality for encoding ODVs; meanwhile, our approach is also effective in the auxiliary task of saliency detection in viewports.

High efficiency video coding (HEVC) has brought outperforming efficiency for video compression. To reduce the compression artifacts of HEVC, we propose a DenseNet based approach as the in-loop filter of HEVC, which leverages multiple adjacent frames to enhance the quality of each encoded frame. Specifically, the higher-quality frames are found by a reference frame selector (RFS). Then, a deep neural network for multi-frame in-loop filter (named MIF-Net) is developed to enhance the quality of each encoded frame by utilizing the spatial information of this frame and the temporal information of its neighboring higher-quality frames. The MIF-Net is built on the recently developed DenseNet, benefiting from the improved generalization capacity and computational efficiency. Finally, experimental results verify the effectiveness of our multi-frame in-loop filter, outperforming the HM baseline and other state-of-the-art approaches.

High Efficiency Video Coding (HEVC) achieves significant coding performance over H.264. However, the performance gain is achieved at the cost of substantially higher encoding complexity, in which the coding tree unit (CTU) partition is one of the most time-consuming parts due to the rate-distortion optimization-based ergodic search of all possible quad-tree partitions. To address this problem, this paper proposes a texture-classification accelerated convolutional neural network (CNN)-based fast intra CU partition scheme to reduce the encoding complexity for intra-coding in HEVC, by taking into consideration of the heterogeneous texture characteristics into the CNN-based classification. First, a threshold-based texture classification model is developed to identify the heterogeneous and homogeneous CTUs, through jointly consideration of the CU depth, quantization parameter and texture complexity. Second, three different CNN structures are designed and trained to predict the CU partition mode for each CU layer in the heterogeneous CTUs. Finally, extensive experimental results show that the proposed scheme can reduce intra-mode encoding time by 62.13% with negligible BD-rate loss of 2.01%, consistently outperforming two state-of-the-art CNN-based schemes in terms of both coding performance and complexity reduction.

HEVC, as the latest video coding standard, achieves top performance on image compression. On the basis of this, we propose a novel approach to optimize subjective quality for HEVC-based image compression. Specifically, a bit allocation formulation is established to optimize subjective quality with constraint on bit-rates. Then, we propose a recursive Taylor expansion method to quickly solve such a formulation with an approximate closed-form solution. The experimental results show the superior performance of our approach, with ~40% BD-rate saving over the state-of-the-art HEVC-MSP for face image compression.