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

Abstract

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.

Intelligent visually lossless compression of dental images

“…The first factor is a used coder and the peculiarities of distortions introduced by it. As known, JPEG introduces blocking effects (artifacts) (Slone et al, 2000;Afnan et al, 2023) and this is undesired [similar effects, but to a lesser degree, can be observed for other coders based on discrete cosine transform (DCT) (Ponomarenko et al, 2005); because of this, image deblocking is often used after decompression]. In turn, wavelet-based coders such as, e.g., JPEG 2000 (Christopoulos et al, 2000) and SPIHT (Kim and Pearlman, 1997) produce ringing artifacts (Punchihewa et al, 2005;Kim et al, 2010;Zhang et al, 2012) and this is undesired as well.…”

“…Noise type and its spatial-spectral properties are taken into consideration in (Lastri et al, 2005;Ponomarenko et al, 2011) to provide invisibility of distortions. Correlation between image quality metrics and distortion visibility threshold has been studied (Kim et al, 2010;Wolski et al, 2018;Afnan et al, 2023). It has been shown that visual quality metrics, both widely known and the ones designed recently (Johnson et al, 2011;Wolski et al, 2018;Afnan et al, 2023) perform better than conventional peak signal-to-noise ratio (PSNR).…”

“…Correlation between image quality metrics and distortion visibility threshold has been studied (Kim et al, 2010;Wolski et al, 2018;Afnan et al, 2023). It has been shown that visual quality metrics, both widely known and the ones designed recently (Johnson et al, 2011;Wolski et al, 2018;Afnan et al, 2023) perform better than conventional peak signal-to-noise ratio (PSNR). Note that the papers (Lastri et al, 2005;Ponomarenko et al, 2011;Võ et al, 2011;Ponomarenko et al, 2013) deal with other than dental types of images.…”

Retinal Image Quality Assessment Using Morphological Operations

“…Retina quality assessment approaches are of two types: Objective methods whose aim is to investigate the presence of some specific features which must be present for compliance to some standards and subjective methods where the quality of an image is done subjectively by an expert thus classifying the image as either of poor quality or of quality [10]. For subjective method, judgement on quality is done as per the point of view of an expert without an explanation on why an image has been classified as of quality or not, meaning it can be classified as of non-quality by another expert, so quality takes different meanings for different experts.…”

Vessel Trajectory Data Compression Algorithm considering Critical Region Identification

“…Te compression evaluation index is an essential basis for evaluating the compression efect of the algorithm [38]. In this paper, the performance of the algorithm is investigated using fve metrics: trajectory similarity (TS), trajectory compression rate (CR), length loss rate (LLR), algorithm running time, and algorithm overall efciency (AOE).…”