Deep Neural Network for Blind Visual Quality Assessment of 4K Content

Lu, Wei; Sun, Weiqiang; Min, Xiongkuo; Zhu, Wenhan; Zhou, Quan; He, Jun; Wang, Qiyuan; Zhang, Zicheng; Wang, Tao; Zhai, Guangtao

doi:10.1109/tbc.2022.3221689

Cited by 24 publications

(3 citation statements)

References 85 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several no-reference IQA frameworks are proposed based on both the traditional and deep learning methods [ 9 , 26 ]. In this regard, Golestaneh et al [ 27 ] presented a transformer and convolutional neural network (CNN)-based assembled method to rank the images based on local and non-local features.…”

Section: Related Workmentioning

confidence: 99%

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

Afnan

Ullah

Yaseen

et al. 2023

Sensors

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%

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

Afnan

Ullah

Yaseen

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…In order to achieve better modeling ability, various neural architecture design strategies are proposed. Lu et al [13] deepen the depth of IQA models, while Zhao et al [14] and Liu et al [15] increase the width of IQA models, i.e., adopting dual-branch architectures. Besides, Zhang et al [16] introduce channel-spatial attention mechanisms into IQA models.…”

Section: A Super-resolution Image Quality Assessmentmentioning

confidence: 99%

“…With the success of the first deep learning-based blind SR IQA measure, researchers shift their focus from feature engineering to neural architecture engineering. In the past few years, various neural architecture strategies including deeper network architecture [13], dual-branch framework [14], [15], and visual attention mechanisms [16], have been proposed to further improve the performance.…”

Section: Introductionmentioning

confidence: 99%