A DenseNet Based Approach for Multi-Frame In-Loop Filter in HEVC
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Computational EfficiencyVideo CompressionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Revisiting the Sample Adaptive Offset post-filter of VVC with Neural-Networks
The Sample Adaptive Offset (SAO) filter has been introduced in HEVC to reduce general coding and banding artefacts in the reconstructed pictures, in complement to the De-Blocking Filter (DBF) which reduces artifacts at b…
BlockingVideo CompressionLearned Quality Enhancement via Multi-Frame Priors for HEVC Compliant Low-Delay Applications
Networked video applications, e.g., video conferencing, often suffer from poor visual quality due to unexpected network fluctuation and limited bandwidth. In this paper, we have developed a Quality Enhancement Network (Q…
DecoderVideo CompressionOn Intra Video Coding and In-loop Filtering for Neural Object Detection Networks
Classical video coding for satisfying humans as the final user is a widely investigated field of studies for visual content, and common video codecs are all optimized for the human visual system (HVS). But are the assump…
Autonomous Drivingobject-detectionObject DetectionvalidMFRNet: A New CNN Architecture for Post-Processing and In-loop Filtering
In this paper, we propose a novel convolutional neural network (CNN) architecture, MFRNet, for post-processing (PP) and in-loop filtering (ILF) in the context of video compression. This network consists of four Multi-lev…
Video CompressionPosition Dependent Prediction Combination For Intra-Frame Video Coding
Intra-frame prediction in the High Efficiency Video Coding (HEVC) standard can be empirically improved by applying sets of recursive two-dimensional filters to the predicted values. However, this approach does not allow …
PositionPrediction