Image Quality Assessment through FSIM, SSIM, MSE and PSNR—A Comparative Study

Sara, Umme; Akter, Morium; Uddin, Mohammad Shorif

doi:10.4236/jcc.2019.73002

Cited by 891 publications

(477 citation statements)

References 12 publications

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“…The FSIM measures the similarities between the features of two images. One study [126] showed that SSIM and FSIM provide perception errors based on the human visual system, while PSNR and MSE provide absolute errors. Therefore, the performance metrics SSIM and FSIM are easy to understand for measuring performance, compared to PSNR and MSE.…”

Section: Performance Metrics For Evaluating Watermarking Systemmentioning

confidence: 99%

Digital Image Watermarking Techniques: A Review

2020

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Digital image authentication is an extremely significant concern for the digital revolution, as it is easy to tamper with any image. In the last few decades, it has been an urgent concern for researchers to ensure the authenticity of digital images. Based on the desired applications, several suitable watermarking techniques have been developed to mitigate this concern. However, it is tough to achieve a watermarking system that is simultaneously robust and secure. This paper gives details of standard watermarking system frameworks and lists some standard requirements that are used in designing watermarking techniques for several distinct applications. The current trends of digital image watermarking techniques are also reviewed in order to find the state-of-the-art methods and their limitations. Some conventional attacks are discussed, and future research directions are given.

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Section: Performance Metrics For Evaluating Watermarking Systemmentioning

confidence: 99%

Digital Image Watermarking Techniques: A Review

2020

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“…In this paper, the image quality is measured by comparing the PSNR of the images. The structures of the noise reduction image and the original image are compared by calculating the structural similarity index measurement (SSIM) [30] between them. The value range of SSIM is [0, 1].…”

Section: B: Image Enhancement Region Definitionmentioning

confidence: 99%

Detection of Apple Defects Based on the FCM-NPGA and a Multivariate Image Analysis

Zhang

Zhou

et al. 2020

IEEE Access

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In existing machine vision technology for fruit defects, the hue appears different, and the defect area is small due to the irregularity of illumination reflection from the surface incident light source, this makes it difficult to extract the defect area. Thus, we proposed an apple defect detection method based on the Fuzzy C-means Algorithm and the Nonlinear Programming Genetic Algorithm (FCM-NPGA) in combination with a multivariate image analysis. First, the image was denoised and enhanced through fractional differentiation. The noise points and edge points were removed, and the important texture information was preserved. Then, the FCM-NPGA algorithm was used to segment the suspicious defect graph. Finally, a method based on a multivariate image analysis strategy was used to detect the flaws of the apple's suspicious defect map. The application results of 2000 images showed that the overall detection accuracy was 98%. Experiments show that the apple defect detection algorithm based on FCM and NPGA combined with multi-image analysis method is effective. INDEX TERMS Apple defect detection, fractional calculus, multivariate image analysis, FCM, NPGA.

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“…Whereas PSNR uses video signals as objective parameters. PSNR compares the signal from each video frame in the video source with each video output frame and measures the difference between them [20]. The ratio of the two videos/images is computed in decibels.…”

Section: Assessment Parametersmentioning

confidence: 99%

Objective Quality Assessment of Multi-Resolution Video based on H.264/AVC and H.265/HEVC Encoding

Rizal¹,

Suharso²,

Abujabbar³

et al. 2020

Proceedings of the Proceedings of the 7th Mathematics, Science, and Computer Science Education International Seminar, MSCEIS 20

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This study aims to carry out an objective assessment by evaluating the encoding performance of the new standard compared to its predecessor H.264/AVC. In addition, to produce a comprehensive investigation, we compared several resolutions (1080p, 720p, and 480p) with a combination of CRF and encoder presets. To find out the performance of both techniques we used several test parameters such as encoding duration, compression ratio, bitrate, MSE, and PSNR. The results showed that H.264 was faster in terms of the time needed to carry out the encoding process. However the compression ratio and bitrate of H.265 was better with a difference of 38.5% and 52.7% respectively. Finally, if the user wants to prioritize better quality video output without having to sacrifice a lot of compression time and small file size, then our recommendation is to use H.265 and by HD resolution configuration (720p) with medium presets and 18 CRF value.

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Image Quality Assessment through FSIM, SSIM, MSE and PSNR—A Comparative Study

Cited by 891 publications

References 12 publications

Digital Image Watermarking Techniques: A Review

Digital Image Watermarking Techniques: A Review

Detection of Apple Defects Based on the FCM-NPGA and a Multivariate Image Analysis

Objective Quality Assessment of Multi-Resolution Video based on H.264/AVC and H.265/HEVC Encoding

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