SWGNet: Step-Wise Reference Frame Generation Network for Multiview Video Coding

Zhang, Jing; Hou, Yonghong; Pan, Zhaoqing; Peng, Bo; Ling, Nam; Lei, Jianjun

doi:10.1109/tcsvt.2023.3312213

Cited by 1 publication

(1 citation statement)

References 37 publications

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“…Recently, deep learning technology has developed rapidly and achieved impressive success in various tasks, such as quality enhancement [9,10], content generation [11,12], and object detection [13,14]. Inspired by this, researchers have explored many deep learningbased video coding methods [15][16][17]. For light field image coding, some end-to-end coding frameworks have also been proposed, and preliminary results have been achieved [18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

End-to-End Light Field Image Compression with Multi-Domain Feature Learning

Ye,

Li,

et al. 2024

Applied Sciences

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Recently, end-to-end light field image compression methods have been explored to improve compression efficiency. However, these methods have difficulty in efficiently utilizing multi-domain features and their correlation, resulting in limited improvement in compression performance. To address this problem, a novel multi-domain feature learning-based light field image compression network (MFLFIC-Net) is proposed to improve compression efficiency. Specifically, an EPI-based angle completion module (E-ACM) is developed to obtain a complete angle feature by fully exploring the angle information with a large disparity contained in the epipolar plane image (EPI) domain. Furthermore, in order to effectively reduce redundant information in the light field image, a spatial-angle joint transform module (SAJTM) is proposed to reduce redundancy by modeling the intrinsic correlation between spatial and angle features. Experimental results demonstrate that MFLFIC-Net achieves superior performance on MS-SSIM and PSNR metrics compared to public state-of-the-art methods.

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