61.2: The 3D Image Quality Index (ΔX3D) Including Crosstalk, Motion Blur, and Disparity for Two-View Stereoscopic Images

2011 ◽  
Vol 42 (1) ◽  
pp. 912-915 ◽  
Author(s):  
Yu-Yi Chien ◽  
Yu-Cheng Chang ◽  
Yi-Pai Huang
Keyword(s):  
Image Quality ◽  
Quality Index ◽  
Motion Blur ◽  
3D Image ◽  
Stereoscopic Images ◽  
Image Quality Index

ANALISIS RESTORASI CITRA KABUR ALGORITMA WIENER MENGGUNAKAN INDEKS KUALITAS CITRA

2017 ◽  
Vol 2 (1) ◽  
Author(s):  
Nur Afiyat
Keyword(s):  
Image Quality ◽  
Image Restoration ◽  
Quality Index ◽  
Input Image ◽  
Motion Blur ◽  
Image Motion ◽  
Original Image ◽  
Degraded Image ◽  
Gaussian Blur ◽  
Image Quality Index

Degradation and additional noise in an image will make the quality decreases. Image restoration is needed to restore the image quality to be similar to the original state. Damage to the image can restored include: blurred image, the image with noise spots, dual image, over-saturated color, and the pixel error. To make theblur image is modeled as a convolution between the original image with the point spread function (PSF) which is a point or object spectrum will be spread out so that objects appear to fade. Image restoration is done by passing a blurry image on a filter. In this study discussed Wiener image restoration algorithm using the input image is degraded motion blur and Gaussian blur. Quality image restoration results were analyzed using the image quality index, by comparing the image of the restoration of the original image as a reference. Further image restoration results used as the input image is then processed using Index Image Analysis GUI application. Each of the input image must have a resolution and dimensions that are identical to a reference image. The results showed that by providing opaqueness different models on the same image, the degree of blurring that occurs will be different. Image quality index results for the restoration of degraded image higher than the Gaussian blur image of the restoration of degraded image motion blur. Image quality index results for the restoration of degraded image motion blur ranged from 0.84229 up to 0.87146. Image quality index results for the restoration of degraded Gaussian blur images ranging from 0.86969 up to 0.90025.Keywords: Restoration, blur image, PSF, Wiener algorithm, the image quality index.

Adaptive DCT coding using the new universal image quality index

2005 ◽  
Author(s):  
Aldo Morales ◽  
Sedig Agili ◽  
Lakshmi P. Baskaran
Keyword(s):  
Image Quality ◽  
Quality Index ◽  
Image Quality Index ◽  
Dct Coding

A NOVEL COMPRESSED IMAGES QUALITY METRIC

2011 ◽  
Vol 11 (02) ◽  
pp. 281-292
Author(s):  
WEN LU ◽  
LIHUO HE ◽  
WENJIAN TANG ◽  
FEI GAO ◽  
WEILONG HOU
Keyword(s):  
Image Quality ◽  
Quality Index ◽  
Image Quality Assessment ◽  
Performance Indicator ◽  
Directional Information ◽  
Quality Metric ◽  
Compressed Images ◽  
Image Processing Algorithms ◽  
Processing Algorithms ◽  
Image Quality Index

As the performance indicator of the image processing algorithms or systems, image quality assessment (IQA) has attracted the attention of many researchers. Aiming to the widely used compression standards, JPEG and JPEG2000, we propose a new no reference (NR) metric for compressed images to do IQA. This metric exploits the causes of distortion by JPEG and JPEG2000, employs the directional discrete cosine transform (DDCT) to obtain the detail and directional information of the images and incorporates with the visual perception to obtain the image quality index. Experimental results show that the proposed metric not only has outstanding performance on JPEG and JPEG2000 images, but also applicable to other types of artifacts.

No-training, no-reference image quality index using perceptual features

2013 ◽  
Vol 52 (5) ◽  
pp. 057003 ◽  
Author(s):  
Chaofeng Li ◽  
Yiwen Ju ◽  
Alan C. Bovik ◽  
Xiaojun Wu ◽  
Qingbing Sang
Keyword(s):  
Image Quality ◽  
Quality Index ◽  
Reference Image ◽  
Perceptual Features ◽  
Image Quality Index

Validation of no-reference image quality index for the assessment of digital mammographic images

2016 ◽  
Author(s):  
Helder C. R. de Oliveira ◽  
Bruno Barufaldi ◽  
Lucas R. Borges ◽  
Salvador Gabarda ◽  
Predrag R. Bakic ◽  
...  
Keyword(s):  
Image Quality ◽  
Quality Index ◽  
Reference Image ◽  
Mammographic Images ◽  
Image Quality Index

scholarly journals Statistical properties of an unassisted image quality index for SAR imagery

2021 ◽  
Author(s):  
L Gomez ◽  
R Ospina ◽  
Alejandro Frery
Keyword(s):  
Image Quality ◽  
Quality Index ◽  
Radar Data ◽  
Statistical Properties ◽  
Test Statistic ◽  
Synthetic Aperture Radar Data ◽  
Ratio Image ◽  
Exploratory Data ◽  
Sar Imagery ◽  
Image Quality Index

© 2019 by the authors. The M estimator is a recently proposed image-quality index used to evaluate the despeckling operation in SAR (Synthetic Aperture Radar) data. It is used also to rank despeckling filters and to improve their design. As a difference with traditional image-quality estimators, it operates not on the filtered result but on a derived one, i.e., the ratio image. However, a deep statistical analysis of its properties remains open and, with it, the ability to use it as a test statistic. In this work, we focus on obtaining insights into its distribution as well as on exploring other remarkable statistical properties of this unassisted estimator. This study is performed through EDA (Exploratory Data Analysis) and the well-known ANOVA (ANalysis Of VAriance). We test our results on a set of simulated SAR data and provide guides to enrich theMestimator to extend its capabilities.

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