Subjective Assessment of Objective Image Quality Metrics Range Guaranteeing Visually Lossless Compression

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 3; p. 1297
• Main Authors: Afnan, Afnan, Ullah, Faiz, Yaseen, Yaseen, Lee, Jinhee, Jamil, Sonain, Kwon, Oh-Jin
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 23.01.2023; MDPI
• Subjects: Algorithms; Human subjects; image compression; image quality assessment; JPEG 1; Mathematical models; Performance evaluation; Quality standards; Sensors; Software; subjective and objective evaluation; visually lossless; Switzerland; Massachusetts; image quality assessment; subjective and objective evaluation; visually lossless; image compression; JPEG 1
• DOI: 10.3390/s23031297
• ISSN: 1424-8220

The usage of media such as images and videos has been extensively increased in recent years. It has become impractical to store images and videos acquired by camera sensors in their raw form due to their huge storage size. Generally, image data is compressed with a compression algorithm and then stored or transmitted to another platform. Thus, image compression helps to reduce the storage size and transmission cost of the images and videos. However, image compression might cause visual artifacts, depending on the compression level. In this regard, performance evaluation of the compression algorithms is an essential task needed to reconstruct images with visually or near-visually lossless quality in case of lossy compression. The performance of the compression algorithms is assessed by both subjective and objective image quality assessment (IQA) methodologies. In this paper, subjective and objective IQA methods are integrated to evaluate the range of the image quality metrics (IQMs) values that guarantee the visually or near-visually lossless compression performed by the JPEG 1 standard (ISO/IEC 10918). A novel “Flicker Test Software” is developed for conducting the proposed subjective and objective evaluation study. In the flicker test, the selected test images are subjectively analyzed by subjects at different compression levels. The IQMs are calculated at the previous compression level, when the images were visually lossless for each subject. The results analysis shows that the objective IQMs with more closely packed values having the least standard deviation that guaranteed the visually lossless compression of the images with JPEG 1 are the feature similarity index measure (FSIM), the multiscale structural similarity index measure (MS-SSIM), and the information content weighted SSIM (IW-SSIM), with average values of 0.9997, 0.9970, and 0.9970 respectively.