Performance Analysis of JPEG XR with Deep Learning-Based Image Super-Resolution-Reference-Cited by-同舟云学术

Performance Analysis of JPEG XR with Deep Learning-Based Image Super-Resolution

Published:2022-11-07 Issue: Volume: Page:
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Container-title:2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)
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Author:

Min Taingliv¹,Aramvith Supavadee¹

Affiliation:

1. Chulalongkorn University,Faculty of Engineering,Multimedia Data Analytics and Processing Research Unit, Department of Electrical Engineering,Bangkok,Thailand,10330

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9979726/9979808/09980019.pdf?arnumber=9980019

Reference12 articles.

1. Performance Comparison of Jpeg, Jpeg XT, Jpeg LS, Jpeg 2000, Jpeg XR, HEVC, EVC and VVC for Images;kumar;2021 6th International Conference for Convergence in Technology (I2CT) I2CT,0

2. JPEG XR an Image Coding Standard

3. Quality and Rate Control of JPEG XR;liu;Diss,2016

4. Large-scale JPEG image steganalysis using hybrid deep-learning framework;zeng;IEEE Transactions on Information Forensics and Security,2017

5. Image super-resolution using deep convolutional networks;dong;IEEE Transactions on Pattern Analysis and Machine Intelligence,2015

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2. Recovering JPEG Compression Loss via Deep Learning-Based Super Resolution Techniques;2023 31st Signal Processing and Communications Applications Conference (SIU);2023-07-05