A Fast Feature Similarity Index for Image Quality Assessment

2015 ◽  
Vol 8 (11) ◽  
pp. 179-194 ◽  
Author(s):  
Shaoping Xu ◽  
Xiaoping Liu ◽  
Shunliang Jiang
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Similarity Index ◽  
Feature Similarity ◽  
Feature Similarity Index

scholarly journals FSIM: A Feature Similarity Index for Image Quality Assessment

2011 ◽  
Vol 20 (8) ◽  
pp. 2378-2386 ◽  
Author(s):  
Lin Zhang ◽  
Lei Zhang ◽  
Xuanqin Mou ◽  
D. Zhang
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Similarity Index ◽  
Feature Similarity ◽  
Feature Similarity Index

ROI Based Post Image Quality Assessment Technique on Multiple Localized Filtering Method On Kinect HD Sensor Mobile Robot

2018 ◽  
Vol 3 (2) ◽  
pp. 145
Author(s):  
Kholilatul Wardani ◽  
Aditya Kurniawan
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Similarity Index ◽  
Assessment Model ◽  
Sensor Data ◽  
Edge Region ◽  
Kinect Sensor ◽  
Image Region ◽  
Filtering Technique

 The ROI (Region of Interest) Image Quality Assessment is an image quality assessment model based on the SSI (Structural Similarity Index) index used in the specific image region desired to be assessed. Output assessmen value used by this image assessment model is 1 which means identical and -1 which means not identical. Assessment model of ROI Quality Assessment in this research is used to measure image quality on Kinect sensor capture result used in Mobile HD Robot after applied Multiple Localized Filtering Technique. The filter is applied to each capture sensor depth result on Kinect, with the aim to eliminate structural noise that occurs in the Kinect sensor. Assessment is done by comparing image quality before filter and after filter applied to certain region. The kinect sensor will be conditioned to capture a square black object measuring 10cm x 10cm perpendicular to a homogeneous background (white with RGB code 255,255,255). The results of kinect sensor data will be taken through EWRF 3022 by visual basic 6.0 program periodically 10 times each session with frequency 1 time per minute. The results of this trial show the same similar index (value 1: identical) in the luminance, contrast, and structural section of the edge region or edge region of the specimen. The value indicates that the Multiple Localized Filtering Technique applied to the noise generated by the Kinect sensor, based on the ROI Image Quality Assessment model has no effect on the image quality generated by the sensor.

scholarly journals No-Reference Blurred Image Quality Assessment by Structural Similarity Index

2018 ◽  
Vol 8 (10) ◽  
pp. 2003 ◽  
Author(s):  
Haopeng Zhang ◽  
Bo Yuan ◽  
Bo Dong ◽  
Zhiguo Jiang
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
State Of The Art ◽  
Similarity Index ◽  
Structural Similarity ◽  
Luminance Contrast ◽  
Structural Similarity Index ◽  
Contrast Structure ◽  
Blurred Image

No-reference (NR) image quality assessment (IQA) objectively measures the image quality consistently with subjective evaluations by using only the distorted image. In this paper, we focus on the problem of NR IQA for blurred images and propose a new no-reference structural similarity (NSSIM) metric based on re-blur theory and structural similarity index (SSIM). We extract blurriness features and define image blurriness by grayscale distribution. NSSIM scores an image quality by calculating image luminance, contrast, structure and blurriness. The proposed NSSIM metric can evaluate image quality immediately without prior training or learning. Experimental results on four popular datasets show that the proposed metric outperforms SSIM and well-matched to state-of-the-art NR IQA models. Furthermore, we apply NSSIM with known IQA approaches to blurred image restoration and demonstrate that NSSIM is statistically superior to peak signal-to-noise ratio (PSNR), SSIM and consistent with the state-of-the-art NR IQA models.

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