earticle

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%.