Benchmarking of objective quality metrics for HDR image quality assessment

Currently available 360° cameras normally capture several images covering a scene in all directions around a shooting point. The captured images are spherical in nature and are mapped to a two-dimensional plane using various projection methods. Many projection formats have been proposed for 360° videos. However, standards for a quality assessment of 360° images are limited. In this paper, various projection formats are compared to explore the problem of distortion caused by a mapping operation, which has been a considerable challenge in recent approaches. The performances of various projection formats, including equi-rectangular, equal-area, cylindrical, cube-map, and their modified versions, are evaluated based on the conversion causing the least amount of distortion when the format is changed. The evaluation is conducted using sample images selected based on several attributes that determine the perceptual image quality. The evaluation results based on the objective quality metrics have proved that the hybrid equi-angular cube-map format is the most appropriate solution as a common format in 360° image services for where format conversions are frequently demanded. This study presents findings ranking these formats that are useful for identifying the best image format for a future standard.

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Entropy-Based Combined Metric for Automatic Objective Quality Assessment of Stitched Panoramic Images

Entropy ◽

10.3390/e23111525 ◽

2021 ◽

Vol 23 (11) ◽

pp. 1525

Author(s):

Krzysztof Okarma ◽

Wojciech Chlewicki ◽

Mateusz Kopytek ◽

Beata Marciniak ◽

Vladimir Lukin

Keyword(s):

Image Quality ◽

Quality Assessment ◽

Image Quality Assessment ◽

Subjective Evaluation ◽

General Purpose ◽

Sensing Applications ◽

Objective Quality ◽

Panoramic Images ◽

Remote Sensing Applications

Quality assessment of stitched images is an important element of many virtual reality and remote sensing applications where the panoramic images may be used as a background as well as for navigation purposes. The quality of stitched images may be decreased by several factors, including geometric distortions, ghosting, blurring, and color distortions. Nevertheless, the specificity of such distortions is different than those typical for general-purpose image quality assessment. Therefore, the necessity of the development of new objective image quality metrics for such type of emerging applications becomes obvious. The method proposed in the paper is based on the combination of features used in some recently proposed metrics with the results of the local and global image entropy analysis. The results obtained applying the proposed combined metric have been verified using the ISIQA database, containing 264 stitched images of 26 scenes together with the respective subjective Mean Opinion Scores, leading to a significant increase of its correlation with subjective evaluation results.

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Edge Detection technique based on HDR image quality assessment

Journal of Physics Conference Series ◽

10.1088/1742-6596/2078/1/012029 ◽

2021 ◽

Vol 2078 (1) ◽

pp. 012029

Author(s):

Dingxian Wang

Keyword(s):

Image Quality ◽

Edge Detection ◽

Quality Assessment ◽

Image Quality Assessment ◽

Dynamic Range ◽

High Dynamic Range ◽

Edge Information ◽

Image Edge Detection ◽

Image Edge ◽

Hdr Image

Abstract Image edge detection is one of the major study aspects in current computer image processing field. The quality of the input images is uneven, some have large fuzzy areas, some are underexposed, and the edges of objects in the images are difficult to detect, and the application scenarios of image edge detection are limited. In the view of the above problems, this paper has proposed that by applying High Dynamic Range (HDR) image quality assessment technology, combining multiple images with different exposures into one HDR image with detailed edge information, This technology effectively solved problem of low edge information richness, improved the effectiveness of edge detection algorithms, and contributed to the development of edge detection technology.

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Deep Learning in Image Quality Assessment: Past, Present, and What Lies Ahead

London Imaging Meeting ◽

10.2352/issn.2694-118x.2021.lim-1 ◽

2021 ◽

Vol 2021 (1) ◽

pp. 1-4

Author(s):

Seyed Ali Amirshahi

Keyword(s):

Image Processing ◽

Computer Vision ◽

Image Quality ◽

Quality Assessment ◽

Image Quality Assessment ◽

Machine Learning Techniques ◽

Short Paper ◽

Subjective Quality Assessment ◽

Learning Techniques ◽

Objective Quality

Quality assessment of images plays an important role in different applications in image processing and computer vision. While subjective quality assessment of images is the most accurate approach due to issues objective quality metrics have been the go to approach. Until recently most such metrics have taken advantage of different handcrafted features. Similar (but with a slower speed) to other applications in image processing and computer vision, different machine learning techniques, more specifically Convolutional Neural Networks (CNNs) have been introduced in different tasks related to image quality assessment. In this short paper which is a supplement to a focal talk given with the same title at the London Imaging Meeting (LIM) 2021 we aim to provide a short timeline on how CNNs have been used in the field of image quality assessment so far, how the field could take advantage of CNNs to evaluate the image quality, and what we expect will happen in the near future.

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