DFCE: Decoder-Friendly Chrominance Enhancement for HEVC Intra Coding

Yang, Renwei; Liu, Hewei; Zhu, Shuyuan; Zheng, Xin; Zeng, Bing

doi:10.1109/tcsvt.2022.3195904

Cited by 5 publications

(4 citation statements)

References 16 publications

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“…where [•] is the concatenation operation and Conv is a convolution layer with C filters whose size is 3 × 3 and followed by a LeakyReLU activation function. Then, F (1)(2)(3) and F (3)(4)(5) are combined together to be sent into the residual channel attention block (RCAB) [27], forming the fused feature -3) , F (3)(4)(5) ]).…”

Section: B Inter-group Feature Fusionmentioning

confidence: 99%

“…Over the past decades, lots of methods have been proposed to implement compressed video quality enhancement. These methods achieve impressive quality improvement, not only for the 2D video [4]- [6] but also for the 3D video [7], [8]. Among these methods, the single-frame-based enhancement methods [4]- [6] were firstly proposed and they utilized an LQ frame to generate HQ frame.…”

Section: Introductionmentioning

confidence: 99%

“…These methods achieve impressive quality improvement, not only for the 2D video [4]- [6] but also for the 3D video [7], [8]. Among these methods, the single-frame-based enhancement methods [4]- [6] were firstly proposed and they utilized an LQ frame to generate HQ frame. Although these methods improved quality for compressed videos, they ignored using the temporal correlation of neighboring frames to construct the enhancement models, which limits their performance.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Deep Video Super-Resolution with Flow-Guided Deformable Alignment and Sparsity-based Temporal-Spatial Enhancement

Zhu

Zhang

Zhu

et al. 2022

2022 IEEE 24th International Workshop on Multimedia Signal Processing (MMSP)

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In this paper, we propose a temporal group alignment and fusion network to enhance the quality of compressed videos by using the long-short term correlations between frames. The proposed model consists of the intra-group feature alignment (IntraGFA) module, the inter-group feature fusion (InterGFF) module, and the feature enhancement (FE) module. We form the group of pictures (GoP) by selecting frames from the video according to their temporal distances to the target enhanced frame. With this grouping, the composed GoP can contain either long-or short-term correlated information of neighboring frames. We design the IntraGFA module to align the features of frames of each GoP to eliminate the motion existing between frames. We construct the InterGFF module to fuse features belonging to different GoPs and finally enhance the fused features with the FE module to generate high-quality video frames. The experimental results show that our proposed method achieves up to 0.05dB gain and lower complexity compared to the state-of-the-art method.

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Section: B Inter-group Feature Fusionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Deep Video Super-Resolution with Flow-Guided Deformable Alignment and Sparsity-based Temporal-Spatial Enhancement

Zhu

Zhang

Zhu

et al. 2022

2022 IEEE 24th International Workshop on Multimedia Signal Processing (MMSP)

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show abstract

“…Finding similarities between the block structures of the various resolutions recorded using the high-resolution approach is the first step in the process. The resulting block structure is used to accelerate the encoding process [18]. The encoding of a low-resolution representation is accelerated by using a prefabricated block structure.…”

Section: Introductionmentioning

confidence: 99%

Quality Enhancement with Frame-wise DLCNN using High Efficiency Video Coding in 5G Networks

Dommeti,

Dharani,

Shasidhar

et al. 2024

SCPE

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In the present situation, applications related to multimedia are discovered to be comfortable with the use of video. The number of end consumers who use video continues to rise every day. People are presently searching for videos with better quality among the ones that are currently available there. This results in the launch and dissemination of HD (high definition) videos. Ultra high-definition (UHD) videos are becoming more and more popular as a result of this advancement and need. However, as video communication keeps expanding, there is an upsurge in network traffic because of the limited bandwidth, especially among smart cities. Different advancement codecs have been suggested to deal with the data stream to overcome this hazardous circumstance. However, the fact that modern UHD videos have huge amounts of data makes the available codecs even more complicated. UHD videos can be processed with the latest improvement codec, H.265/High-efficiency video coding (HEVC). Nevertheless, it is impacted by increased power consumption and intricate calculations. Limitations in the codec's functionality confine its use to specific applications, preventing its application in wireless, mobile, or portable settings. Hence, this research concentrates on implementing frame-level quality enhancement through a deep learning network known as FQE-Net. The deep learning convolutional neural network (DLCNN) is specifically crafted to manage films with resolutions up to 16K. Its primary objectives include reducing complexity, minimizing artifacts, enhancing the efficiency of the HEVC codec, and compacting energy consumption. To achieve superior efficiency, it is imperative to replace the DWT transforms within the HEVC codec with a DLCNN model. Additionally, incorporating the Content Block Search Algorithm for Motion Estimation and Compensation, alongside filtering techniques like Sample Adaptive Filter and Deblocking Filter, becomes essential. The simulation results showed that the suggested FQE-Net performed better than the conventional techniques.

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Model-based portrait video compression with spatial constraint and adaptive pose processing

Chen,

Lei,

Zhang

et al. 2024

Multimedia Systems

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DFCE: Decoder-Friendly Chrominance Enhancement for HEVC Intra Coding

Cited by 5 publications

References 16 publications

Deep Video Super-Resolution with Flow-Guided Deformable Alignment and Sparsity-based Temporal-Spatial Enhancement

Deep Video Super-Resolution with Flow-Guided Deformable Alignment and Sparsity-based Temporal-Spatial Enhancement

Quality Enhancement with Frame-wise DLCNN using High Efficiency Video Coding in 5G Networks

Model-based portrait video compression with spatial constraint and adaptive pose processing

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