Earticle

초록

영어

Lossy image compression provides an efficient solution to the exchange and storage of image data for consumer applications. The design of lossy algorithms is based on a principle to discard information that are not perceivable by human visual system (HVS). With the popularity of deep learning models (DL) in computer vision (CV), it is necessary to characterize the loss in image quality with respect to computer vision systems as well. Recent studies have analyzed the image distortions resulted from blur and noise, mainly from an adversarial attack perspective. However, fewer studies have dealt with the lossy nature of the JPEG algorithm. Therefore, the current study presents a quantitative assessment of different types of data loss that occurs due to chroma subsampling, quantization, and rounding functions of the JPEG algorithm. In addition, we have analyzed impact of different interpolation methods that are used for chroma upsampling. The analysis have shown that for compression savings, performing either subsampling or quantization preserved the model accuracy while their combination degraded the accuracy by 6%.

목차

Abstract
I. INTRODUCTION
II. METHODS
A. JPEG Compression Standard
B. Lossy Components of the JPEG Algorithm
III. RESULTS AND DISCUSSION
A. Compression Analysis
B. Accuracy Analysis
IV. CONCLUSION
REFERENCES

키워드

저자

• Ijaz Ahmad [ Department of Computer Engineering Chosun University ]
• Seokjoo Shin [ Department of Computer Engineering Chosun University ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004