Derin Öǧrenme Tabanli Süper Çözünürlük Teknikleri Kullanarak JPEG Sikiştirma Kaybinin Iyileştirilmesi

Muhammet Bolat; Nurullah Çalik; Lutfiye Durak Ata

doi:10.1109/SIU59756.2023.10223843

Derin Öǧrenme Tabanli Süper Çözünürlük Teknikleri Kullanarak JPEG Sikiştirma Kaybinin Iyileştirilmesi

Translated title of the contribution: Recovering JPEG Compression Loss via Deep Learning-Based Super Resolution Techniques

Muhammet Bolat, Nurullah Çalik, Lutfiye Durak Ata

Informatics Institute

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This study investigates the use of JPEG, a lossy compression algorithm, for efficient utilization of channel bandwidth. To reduce losses caused by the removal of high-frequency components in compressed images, super-resolution models are developed using deep learning methods. Specifically, the SRCNN, VDSR, and SRDenseNet models are trained from scratch using compressed images. The effects of different compression ratios on images are analyzed in terms of their impact on the recovery of high-frequency components by the super-resolution models. The performance of the models is evaluated using PSNR and SSIM metrics, considering various compression ratios.

Translated title of the contribution	Recovering JPEG Compression Loss via Deep Learning-Based Super Resolution Techniques
Original language	Turkish
Title of host publication	31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350343557
DOIs	https://doi.org/10.1109/SIU59756.2023.10223843
Publication status	Published - 2023
Event	31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023 - Istanbul, Turkey Duration: 5 Jul 2023 → 8 Jul 2023

Publication series

Name	31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023

Conference

Conference	31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023
Country/Territory	Turkey
City	Istanbul
Period	5/07/23 → 8/07/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Access to Document

10.1109/SIU59756.2023.10223843

Cite this

Bolat, M., Çalik, N., & Ata, L. D. (2023). Derin Öǧrenme Tabanli Süper Çözünürlük Teknikleri Kullanarak JPEG Sikiştirma Kaybinin Iyileştirilmesi. In 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023 (31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SIU59756.2023.10223843

Bolat, Muhammet ; Çalik, Nurullah ; Ata, Lutfiye Durak. / Derin Öǧrenme Tabanli Süper Çözünürlük Teknikleri Kullanarak JPEG Sikiştirma Kaybinin Iyileştirilmesi. 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023).

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title = "Derin {\"O}ǧrenme Tabanli S{\"u}per {\c C}{\"o}z{\"u}n{\"u}rl{\"u}k Teknikleri Kullanarak JPEG Siki{\c s}tirma Kaybinin Iyile{\c s}tirilmesi",

abstract = "This study investigates the use of JPEG, a lossy compression algorithm, for efficient utilization of channel bandwidth. To reduce losses caused by the removal of high-frequency components in compressed images, super-resolution models are developed using deep learning methods. Specifically, the SRCNN, VDSR, and SRDenseNet models are trained from scratch using compressed images. The effects of different compression ratios on images are analyzed in terms of their impact on the recovery of high-frequency components by the super-resolution models. The performance of the models is evaluated using PSNR and SSIM metrics, considering various compression ratios.",

keywords = "DCT, Deep Learning, JPEG, SRCNN, SRDenseNet, Super Resolution, VDSR",

author = "Muhammet Bolat and Nurullah {\c C}alik and Ata, {Lutfiye Durak}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023 ; Conference date: 05-07-2023 Through 08-07-2023",

year = "2023",

doi = "10.1109/SIU59756.2023.10223843",

language = "T{\"u}rk{\c c}e",

series = "31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023",

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Bolat, M, Çalik, N & Ata, LD 2023, Derin Öǧrenme Tabanli Süper Çözünürlük Teknikleri Kullanarak JPEG Sikiştirma Kaybinin Iyileştirilmesi. in 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023. 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023, Institute of Electrical and Electronics Engineers Inc., 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023, Istanbul, Turkey, 5/07/23. https://doi.org/10.1109/SIU59756.2023.10223843

Derin Öǧrenme Tabanli Süper Çözünürlük Teknikleri Kullanarak JPEG Sikiştirma Kaybinin Iyileştirilmesi. / Bolat, Muhammet; Çalik, Nurullah; Ata, Lutfiye Durak.
31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Derin Öǧrenme Tabanli Süper Çözünürlük Teknikleri Kullanarak JPEG Sikiştirma Kaybinin Iyileştirilmesi

AU - Bolat, Muhammet

AU - Çalik, Nurullah

AU - Ata, Lutfiye Durak

PY - 2023

Y1 - 2023

N2 - This study investigates the use of JPEG, a lossy compression algorithm, for efficient utilization of channel bandwidth. To reduce losses caused by the removal of high-frequency components in compressed images, super-resolution models are developed using deep learning methods. Specifically, the SRCNN, VDSR, and SRDenseNet models are trained from scratch using compressed images. The effects of different compression ratios on images are analyzed in terms of their impact on the recovery of high-frequency components by the super-resolution models. The performance of the models is evaluated using PSNR and SSIM metrics, considering various compression ratios.

AB - This study investigates the use of JPEG, a lossy compression algorithm, for efficient utilization of channel bandwidth. To reduce losses caused by the removal of high-frequency components in compressed images, super-resolution models are developed using deep learning methods. Specifically, the SRCNN, VDSR, and SRDenseNet models are trained from scratch using compressed images. The effects of different compression ratios on images are analyzed in terms of their impact on the recovery of high-frequency components by the super-resolution models. The performance of the models is evaluated using PSNR and SSIM metrics, considering various compression ratios.

KW - DCT

KW - Deep Learning

KW - JPEG

KW - SRCNN

KW - SRDenseNet

KW - Super Resolution

KW - VDSR

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DO - 10.1109/SIU59756.2023.10223843

M3 - Konferans katkısı

AN - SCOPUS:85173440794

T3 - 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023

BT - 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023

Y2 - 5 July 2023 through 8 July 2023

ER -

Bolat M, Çalik N, Ata LD. Derin Öǧrenme Tabanli Süper Çözünürlük Teknikleri Kullanarak JPEG Sikiştirma Kaybinin Iyileştirilmesi. In 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023. Institute of Electrical and Electronics Engineers Inc. 2023. (31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023). doi: 10.1109/SIU59756.2023.10223843

Derin Öǧrenme Tabanli Süper Çözünürlük Teknikleri Kullanarak JPEG Sikiştirma Kaybinin Iyileştirilmesi

Abstract

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