Flow-Guided Temporal-Spatial Network for HEVC Compressed Video Quality Enhancement

Meng, Xiandong; Deng, Xuan; Zhu, Shuyuan; Liu, Shuaicheng; Zeng, Bing

doi:10.1109/dcc47342.2020.00064

Cited by 2 publications

(2 citation statements)

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“…Temporal alignment plays an important role in multiframe based methods. Several methods compute optical flow to estimate the motion between video frames for alignment [61,19,37,36,35]. The Multi-Frame Quality Enhancement (MFQE) [61] network and MFQE 2.0 [19] improve the quality by utilizing nearest Peak Quality Frames (PQFs) that have higher quality.…”

Section: Related Workmentioning

confidence: 99%

CBREN: Convolutional Neural Networks for Constant Bit Rate Video Quality Enhancement

Zhao

Zheng

Yuan

et al. 2022

IEEE Trans. Circuits Syst. Video Technol.

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Constant bit rate (CBR) videos are widely used in streaming playback applications. However, the image quality of the CBR video is often unstable, especially for scenes with large motion. To this end, we design a new model to represent the distortion of High Efficiency Video Coding (HEVC) constant bit rate video, and propose a neural network for a constant bit rate video quality enhancement (CBREN). We propose a dual-domain restoration module (DRM) to jointly learn the prior knowledge in the pixel domain and the frequency domain. To address the degradation resulting from compression, we propose a twostep quantization degradation estimation strategy. The Inverse DCT (IDCT) Translation Unit (ITU) is used to constrain the quantization table of the constant bit rate video to a suitable range, and the Dynamic Alpha Unit (DAU) is used to finetune the quantization table according to the content of each frame. In order to effectively reduce the block distortion of different sizes produced in the compression process, we adopt a multi-scale network. Extensive experiments show that our approach can greatly enhance the quality of CBR compressed video. Moreover, our method can also be applied to constant quantization parameter (CQP) video enhancement tasks, and is certainly superior to existing methods.

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Section: Related Workmentioning

confidence: 99%

CBREN: Convolutional Neural Networks for Constant Bit Rate Video Quality Enhancement

Zhao

Zheng

Yuan

et al. 2022

IEEE Trans. Circuits Syst. Video Technol.

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show abstract

“…This frame with computed motions is then used as input to the QE network along with the reconstructed frame. In another research, a flowguided network is proposed, where the motion field is extracted from previous and next frames using FlowNet [48,65]. Once the motion compensation is completed, a multi-scale network is applied to extract spatial and temporal features from the input.…”

Section: B Multi-frame Quality Enhancementmentioning

confidence: 99%

A CNN-Based Prediction-Aware Quality Enhancement Framework for VVC

Nasiri

Hamidouche

Morin

et al. 2021

IEEE Open J. Signal Process.

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This paper presents a framework for Convolutional Neural Network (CNN)-based quality enhancement task, by taking advantage of coding information in the compressed video signal. The motivation is that normative decisions made by the encoder can significantly impact the type and strength of artifacts in the decoded images. In this paper, the main focus has been put on decisions defining the prediction signal in intra and inter frames. This information has been used in the training phase as well as input to help the process of learning artifacts that are specific to each coding type. Furthermore, to retain a low memory requirement for the proposed method, one model is used for all Quantization Parameters (QPs) with a QP-map, which is also shared between luma and chroma components. In addition to the Post Processing (PP) approach, the In-Loop Filtering (ILF) codec integration has also been considered, where the characteristics of the Group of Pictures (GoP) are taken into account to boost the performance. The proposed CNN-based Quality Enhancement (QE) framework has been implemented on top of the Versatile Video Coding (VVC) Test Model (VTM-10). Experiments show that the prediction-aware aspect of the proposed method improves the coding efficiency gain of the default CNN-based QE method by 1.52%, in terms of BD-BR, at the same network complexity compared to the default CNN-based QE filter.

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Flow-Guided Temporal-Spatial Network for HEVC Compressed Video Quality Enhancement

Cited by 2 publications

References 0 publications

CBREN: Convolutional Neural Networks for Constant Bit Rate Video Quality Enhancement

CBREN: Convolutional Neural Networks for Constant Bit Rate Video Quality Enhancement

A CNN-Based Prediction-Aware Quality Enhancement Framework for VVC

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