Utilizing image scales towards totally training free blind image quality assessment

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Engineering

Logic Mind Technologies
Vijayangar (Near Maruthi Medicals), Bangalore-40
Ph: 8123668124 // 8123668066
Title: Utilizing Image Scales Towards
TotallyTraining Free Blind Image Quality
Assessment
Abstract—A new approach to blind image quality assessment (BIQA), requiring
no training, is proposed in this paper. The approach is named as blind image
quality evaluator based on scales and works by evaluating the global difference of
the query image analyzed at different scales with the query image at original
resolution. The approach is based on the ability of the natural images to exhibit
redundant information over various scales. A distorted image is considered as a
deviation from the natural image and bereft of the redundancy present in the
original image. The similarity of the original resolution image with its down-scaled
version will decrease more when the image is distorted more. Therefore, the
dissimilarities of an image with its low-resolution versions are cumulated in the
proposed method. We dissolve the query image into its scale-space and measure
the global dissimilarity with the co-occurrence histograms of the original and its
scaled images. These scaled images are the low pass versions of the original image.
The dissimilarity, called low pass error, is calculated by comparing the low pass
versions across scales with the original image. The high pass versions of the image
in different scales are obtained by Wavelet decomposition and their dissimilarity
fromthe original image is also calculated. This dissimilarity, calledhigh pass error,
is computed with the variance and gradient histograms and weighted by the
contrast sensitivity function to make it perceptually effective. These two kinds of

dissimilarities are combined together to derive the quality score of the query
image. This method requires absolutely no training with the distorted image,
pristine images, or subjective human scores to predict the perceptual quality but
uses the intrinsic global change of the query image across scales. The performance
of the proposed method is evaluated across six publicly available databases and
found to be competitive with the state-of-the-art techniques.
Software & Hardware requirement:
1. Hardware requirement:
1. PC
2. RAM minimum 2GB
3. HDD minimum 100GB
2. Software requirement:
1. MATLAB 7.0
2. Signal processing toolbox
3. Image processing toolbox
4. Mathematical toolbox
PROJECT FLOW:
First Review:
Literature Survey
Paper Explanation
Design of Project
Project Enhancement explanation
Second Review:
Implementing 40% of Base Paper

Third Review
Implementing Remaining 60% of Base Paper with Future Enhancement
(Modification)
For More Details please contact
Logic Mind Technologies
Vijayangar (Near Maruthi Medicals), Bangalore-40
Ph: 8123668124 // 8123668066
Mail: logicmindtech@gmail.com

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Utilizing image scales towards totally training free blind image quality assessment

1. Logic Mind Technologies Vijayangar (Near Maruthi Medicals), Bangalore-40 Ph: 8123668124 // 8123668066 Title: Utilizing Image Scales Towards TotallyTraining Free Blind Image Quality Assessment Abstract—A new approach to blind image quality assessment (BIQA), requiring no training, is proposed in this paper. The approach is named as blind image quality evaluator based on scales and works by evaluating the global difference of the query image analyzed at different scales with the query image at original resolution. The approach is based on the ability of the natural images to exhibit redundant information over various scales. A distorted image is considered as a deviation from the natural image and bereft of the redundancy present in the original image. The similarity of the original resolution image with its down-scaled version will decrease more when the image is distorted more. Therefore, the dissimilarities of an image with its low-resolution versions are cumulated in the proposed method. We dissolve the query image into its scale-space and measure the global dissimilarity with the co-occurrence histograms of the original and its scaled images. These scaled images are the low pass versions of the original image. The dissimilarity, called low pass error, is calculated by comparing the low pass versions across scales with the original image. The high pass versions of the image in different scales are obtained by Wavelet decomposition and their dissimilarity fromthe original image is also calculated. This dissimilarity, calledhigh pass error, is computed with the variance and gradient histograms and weighted by the contrast sensitivity function to make it perceptually effective. These two kinds of

2. dissimilarities are combined together to derive the quality score of the query image. This method requires absolutely no training with the distorted image, pristine images, or subjective human scores to predict the perceptual quality but uses the intrinsic global change of the query image across scales. The performance of the proposed method is evaluated across six publicly available databases and found to be competitive with the state-of-the-art techniques. Software & Hardware requirement: 1. Hardware requirement: 1. PC 2. RAM minimum 2GB 3. HDD minimum 100GB 2. Software requirement: 1. MATLAB 7.0 2. Signal processing toolbox 3. Image processing toolbox 4. Mathematical toolbox PROJECT FLOW: First Review: Literature Survey Paper Explanation Design of Project Project Enhancement explanation Second Review: Implementing 40% of Base Paper

3. Third Review Implementing Remaining 60% of Base Paper with Future Enhancement (Modification) For More Details please contact Logic Mind Technologies Vijayangar (Near Maruthi Medicals), Bangalore-40 Ph: 8123668124 // 8123668066 Mail: logicmindtech@gmail.com

Utilizing image scales towards totally training free blind image quality assessment

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