A New HEVC In-Loop Filter Based on Multi-channel Long-Short-Term Dependency Residual Networks

Meng, Xiandong; Chen, Chen; Zhu, Shuyuan; Zeng, Bing

doi:10.1109/dcc.2018.00027

Cited by 41 publications

(18 citation statements)

References 11 publications

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“…Compared to ARCNN, this method can improve PSNR and reduce the number of parameters using small filter size. Meng et al have proposed a Multi-channel Long-Short-term Dependency Residual Network (MLSDRN), which updates each cell to adaptively store and select long-term and short-term dependency information in HEVC [15]. Aforementioned image and video denoising networks can be deployed in the preprocessing of various high-level computer vision applications, such as object recognition [30][31][32] and detection [33,34] to achieve higher accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…Deep learning methods using Convolutional Neural Network (CNN) have brought great potentials into low-level computer vision applications such as Super Resolution (SR) [2][3][4][5][6][7][8], image denoising [9][10][11][12][13][14][15][16], and image colorization [17,18]. In particular, these applications have been developed by CNN-based image denoising methods with deeper and denser network architectures [19,20].…”

Section: Introductionmentioning

confidence: 99%

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Artifacts Reduction Using Multi-Scale Feature Attention Network in Compressed Medical Images

Kim¹,

Jun²

2022

Computers, Materials &Amp; Continua

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Medical image compression is one of the essential technologies to facilitate real-time medical data transmission in remote healthcare applications. In general, image compression can introduce undesired coding artifacts, such as blocking artifacts and ringing effects. In this paper, we proposed a Multi-Scale Feature Attention Network (MSFAN) with two essential parts, which are multi-scale feature extraction layers and feature attention layers to efficiently remove coding artifacts of compressed medical images. Multiscale feature extraction layers have four Feature Extraction (FE) blocks. Each FE block consists of five convolution layers and one CA block for weighted skip connection. In order to optimize the proposed network architectures, a variety of verification tests were conducted using validation dataset. We used Computer Vision Center-Clinic Database (CVC-ClinicDB) consisting of 612 colonoscopy medical images to evaluate the enhancement of image restoration. The proposed MSFAN can achieve improved PSNR gains as high as 0.25 and 0.24 dB on average compared to DnCNN and DCSC, respectively.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Artifacts Reduction Using Multi-Scale Feature Attention Network in Compressed Medical Images

Kim¹,

Jun²

2022

Computers, Materials &Amp; Continua

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“…For example, Jia et al [167] incorporated the colocated block information for in-loop filtering. Meng et al [168] utilized the coding unit partition for further performance improvement. Li and Yu [169] input both the reconstructed frame and the difference between the reconstructed and predicted pixels to improve the coding efficiency.…”

Section: A In-loop Filteringmentioning

confidence: 99%

Advances in Video Compression System Using Deep Neural Network: A Review and Case Studies

Ding

Chen

et al. 2021

Proc. IEEE

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Significant advances in video compression systems have been made in the past several decades to satisfy the near-exponential growth of Internet-scale video traffic. From the application perspective, we have identified three major functional blocks, including preprocessing, coding, and postprocessing, which have been continuously investigated to maximize the end-user quality of experience (QoE) under a limited bit rate budget. Recently, artificial intelligence (AI)-powered techniques have shown great potential to further increase the efficiency of the aforementioned functional blocks, both individually and jointly. In this article, we review recent technical advances in video compression systems extensively, with an emphasis on deep neural network (DNN)based approaches, and then present three comprehensive case studies. On preprocessing, we show a switchable texturebased video coding example that leverages DNN-based scene understanding to extract semantic areas for the improvement Manuscript

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“…Yang et al [55], [56] proposed the Quality Enhancement CNN (QE-CNN) method, in which QE-CNN-I and QE-CNN-P models are trained to reduce the distortions of HEVC I and P frames, respectively. Meng et al [57] proposed the multichannel long-short term dependency residual network (MLS-DRN), which introduces the update cell that can select the short-term and long-term dependency information adaptively. Yang et al [58] investigated the quality fluctuation existing across compressed video frames and enhanced the low quality frames using the neighboring high quality frames.…”

Section: Cnn-based Quality Enhancementmentioning

confidence: 99%

Light Field Image Compression via CNN-Based EPI Super-Resolution and Decoder-Side Quality Enhancement

Zhao

Yang

et al. 2019

IEEE Access

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Because of the capacity of capturing both the spatial and angular information of the light rays simultaneously, light field images (LFIs) contain richer scene information compared with conventional images, but at the cost of huge volume. This paper proposes a novel LFI sparse compression framework driven by convolutional neural network (CNN). The epipolar plane image (EPI) super-resolution is for compensating the information loss caused by sparse sampling and the decoder-side sub-aperture images (SAIs) quality enhancement is for compensating the information loss caused by lossy compression. Specifically, we choose those SAIs both in odd rows and odd columns as our key SAIs and compress them using standard video encoder. For those non-key SAIs, we predict them using decompressed key SAIs by taking advantage of the special structure of EPI. The low-resolution EPIs generated from the sparse SAIs are super-resolved by a CNN and the outputs, high-resolution EPIs, are used to rebuild the dense SAIs. Moreover, in order to improve the quality of the predicted SAI, we add decoder-side quality enhancement before prediction. We propose a multi-scale dense residual network (MSDRN) to implement both EPI super-resolution and quality enhancement. Transfer learning strategies are used to improve the training performance of quality enhancement. The experimental results show the superior performance of the proposed framework over existing methods in terms of rate-distortion performance.INDEX TERMS Light field image, sparse compression, EPI super-resolution, quality enhancement, convolutional neural network.

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A New HEVC In-Loop Filter Based on Multi-channel Long-Short-Term Dependency Residual Networks

Cited by 41 publications

References 11 publications

Artifacts Reduction Using Multi-Scale Feature Attention Network in Compressed Medical Images

Artifacts Reduction Using Multi-Scale Feature Attention Network in Compressed Medical Images

Advances in Video Compression System Using Deep Neural Network: A Review and Case Studies

Light Field Image Compression via CNN-Based EPI Super-Resolution and Decoder-Side Quality Enhancement

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