A Convolutional Neural Network Approach for Post-Processing in HEVC Intra Coding
Yuanying Dai,Dong Liu,Feng Wu +2 more
TLDR
In this article, a Variable-filter-size Residue-learning CNN (VRCNN) was proposed to improve the performance and to accelerate network training for High Efficiency Video Coding.Abstract:
Lossy image and video compression algorithms yield visually annoying artifacts including blocking, blurring, and ringing, especially at low bit-rates. To reduce these artifacts, post-processing techniques have been extensively studied. Recently, inspired by the great success of convolutional neural network (CNN) in computer vision, some researches were performed on adopting CNN in post-processing, mostly for JPEG compressed images. In this paper, we present a CNN-based post-processing algorithm for High Efficiency Video Coding (HEVC), the state-of-the-art video coding standard. We redesign a Variable-filter-size Residue-learning CNN (VRCNN) to improve the performance and to accelerate network training. Experimental results show that using our VRCNN as post-processing leads to on average 4.6% bit-rate reduction compared to HEVC baseline. The VRCNN outperforms previously studied networks in achieving higher bit-rate reduction, lower memory cost, and multiplied computational speedup.read more
Citations
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Journal ArticleDOI
Image and Video Compression With Neural Networks: A Review
TL;DR: The evolution and development of neural network-based compression methodologies are introduced for images and video respectively and the joint compression on semantic and visual information is tentatively explored to formulate high efficiency signal representation structure for both human vision and machine vision.
Journal ArticleDOI
Enhancing Quality for HEVC Compressed Videos
TL;DR: A quality enhancement convolutional neural network (QE-CNN) method that does not require any modification of the encoder to achieve quality enhancement for HEVC, and a time-constrained quality enhancement optimization (TQEO) scheme.
Journal ArticleDOI
Content-Aware Convolutional Neural Network for In-Loop Filtering in High Efficiency Video Coding
TL;DR: This paper quantitatively analyzes the structure of the proposed CNN model from multiple dimensions to make the model interpretable and optimal for CNN-based loop filtering for high-efficiency video coding (HEVC).
Journal ArticleDOI
Residual Highway Convolutional Neural Networks for in-loop Filtering in HEVC.
TL;DR: Experimental results demonstrate that the proposed RHCNN is able to not only raise the PSNR of reconstructed frame but also prominently reduce the bit-rate compared with HEVC reference software.
Journal ArticleDOI
Fully Connected Network-Based Intra Prediction for Image Coding
TL;DR: This paper proposes using a fully connected network to learn an end-to-end mapping from neighboring reconstructed pixels to the current block to generate better prediction using traditional single line-based methods.
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