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Image Quality Evaluation Techniques Using Gabor Filters

A Review ON IMAGE QUALITY Analysis TECHNIQUES USING GABOR FILTERS

  • Deepa Maria Thomas, S. John Livingson

(DEEPA MARIA THOMAS, ROOM NO 303, DMR RESIDENCE, KARUNYA Gals HOSTEL, KARUNYA School, COIMBATORE-641114)

Abstract-Image quality examination has a very important role, especially because the impact that the grade of images have on a viewers is significant. This helps it be important that visual information is assessed for quality once in a while. Images can be distorted with different kinds of irregularities like sound, blur etc. No- guide image quality examination methods does not need a reference point image for examination, this is particularly helpful when there is no guide image available. Gabor filtration systems are effective is evaluating image quality because their rate of recurrence and orientation representations is nearly the same as the human visual system. This is why Gabor filters are used in feature extraction, target recognition as well as feel segmentation. This paper is a study of some of the no- guide image quality assessment methods that use Gabor filtration systems in their quality assessment methodology wither for feature removal or texture analysis.

Key words-image quality evaluation, Gabor filtration, no research quality diagnosis.

INTRODUCTION

Images of good quality have come to be of great importance in our day to day life. Statistics suggest that the average person results in 400 to 600 advertising per day. Pictures form a significant portion of advertisements. Advertisement is merely one area which makes use of images.

There are a whole lot of image quality evaluation techniques available today. No-reference image quality evaluation (NR-IQA) is one of the types where the quality is predicted without the utilization of any reference point image, whereas full guide image quality assessment (FR-IQA) make use of a research image for quality diagnosis. Gabor filter is generally used for advantage detection and it has the benefit that the rate of recurrence and orientation representations are incredibly similar to the human aesthetic system. Or in other words, the image evaluation by Gabor functions is similar to the human understanding. A set of Gabor filtration systems with different frequencies and orientations are also helpful for extracting useful features from a graphic.

GABOR Filter systems IN FEATURE EXTRACTION

Use of Gabor filter is motivated by the actual fact they are optimal with time and frequency. In addition these filtration systems can approximate the aesthetic cortex of some mammals as defined in [1]. That is why Gabor filters are being used in many applications like aim for recognition, image segmentation etc. .

Figure 1: Two dimensional Gabor filter

Sources: http://en. wikipedia. org/wiki/Gabor_filter

NR-IQA USING GABOR FILTERS

No-reference image quality assessment is one of the types where the quality is approximated without the utilization of any guide image, whereas full- reference techniques employ a reference point image for quality examination. Described here are two no- reference point image quality analysis techniques using Gabor filters.

NR-IQA USING VISUAL CODEBOOK (CBIQ)

The first step of the method [2] is codebook building. It is built by dividing an image into BxB patches. All the constant areas are removed, for the rest of the areas Gabor feature vectors are computed. This is repeated for all the training images. By using this place, with a clustering algorithm the codebook is created. The insight image is displayed by the distribution of codewords from the codebook. The amount of times the codeword is available and each time a nearest neighbor is available, the count number is increased by one. If the distance between your vector of the feature and the nearest neighbor is bigger than a predefined threshold, then it is recognized as an outlier. In a case where a huge range of outliers are encountered, then there might be some type of distortion that was not came across in the training set. This image quality evaluation technique is displayed as CBIQ (Codebook Image Quality). The product quality metric is Qm(I) which is distributed by,

Qm(I)=

where,

H1(i) is the likelihood of the event of the code words

DMOS(C(i)) is the Differential Mean Impression Ratings of the codewords.

NR-IQA BASED ON VISUAL SALIENCY GUIDED SAMPLING (IQVG)

This method [3] is a no-reference image quality diagnosis method based on visual saliency. Visual saliency is exactly what grabs our attention and it creates some elements of the image stand out from the others. In this technique firstly, an adequate number of areas are sampled for which the mean saliency is greater than the threshold. Next, feature extraction is done by convolving each patch with Gabor filters. Using histograms the features are encoded, thus giving an image representation. Using regression methods such as SVR the model can learn. Finally, the grade of the test image is expected automatically with a trained model.

FR-IQA USING GABOR FILTERS

The full research approach to image quality diagnosis is different from the no guide methods for the reason that it does not employ a guide image for quality evaluation. Described here are two full research image quality evaluation techniques using Gabor filtration systems.

FR-IQA USING FEATURE SIMILARITY INDEX (FSIM)

In this method [4] first of all, two image extractions are created namely, phase congruence (PC) and gradient magnitude (GM). Laptop or computer is distinction invariant, this implies that the variations in quality credited to contrast dissimilarities are not recognized by PC. As a result of this, the GM must be extracted using gradient providers like Prewitt operator, Sobel operator and Scharr operator. After the Laptop or computer and GM are extracted for the reference image and the distorted image, FSIM can be computed to measure the similarity between the two images. The FSM can be determined by merging similarity solution between images for both Computer and GM given by

where,

is the similarity measurement of PC

is the similarity way of measuring of GM

and are positive real numbers

The mixed similarity is given by

=.

where,

О± and О are guidelines to adapt their comparative weightage or importance.

Finally, the FSIM solution is given as below

FSIM=

where,

FR-IQA USING PERCEPTUAL METHOD (MIGF)

One of the features necessary for good IQA is the fact it should be steady with the subjective view of humans on the image. In this method [5] first, the features are extracted by using a two dimensional Gabor filtration system which operates as an area band-pass filtration system with optimum localization properties. Next, divisive normalization transform (DNTF) is performed where the linear transform coefficient is normalized by the power of the cluster of neighboring coefficients. This reduces the bigger order dependencies in the extracted Gabor features. Next, the visual energy information (VEI) for every size and orientation is given by

where,

О» is the size, Оё is the orientation

is real part of DNTF

is the imaginary part of DNTF

Once the VEI is determined, the mutual information (MI) can be calculated as the difference between your VEI extracted from the research image and distorted image. MI can be computed using marginal possibility syndication and joint probability distribution. The quality score is really as described below

Score=

where,

and denote the VEI of the research image and distorted image respectively at range i and orientation Оё.

COMPARISON OF IQA TECHNIQUES:

The desk below shows a comparison between the four techniques identified above. It describes the merits and demerits of the four IQA methods.

TABLE - 1

COMPARISON TABLE

IQA

MERITS

DEMERITS

CBIQ

This method may be used to put into practice parallel computation.

This method can be prolonged to any kind of distortion

The cost of computation raises sub linearly with the increase of codebook size.

IQVG

IQVG uses the distribution of local feature to quantize, this reduces cost of computation since codebooks aren't used.

IQVG provided just a little smaller performance than CBIQ for JPEG images.

FSIM

The method has high relationship with the subjective examination.

This method provides better performance than IQVG and CBIQ

This method uses a reference point image for IQA.

MIGF

The method has a higher relationship with the subjective analysis.

This method provides better performance than most other IQA of structural similarity algorithms

This method runs on the guide image for IQA.

CONCLUSIONS

This work supplies the comparative study of a few of the IQA methods in image processing. The algorithms that were considered were both no - guide and full - reference algorithms. All the IQA methods reviewed here employ Gabor filters in a single way or another. This newspaper highlights importance of Gabor Filter systems in image quality analysis.

REFERENCES:

[1] Anjali G. (2012), "For image advancement and segmentation by using evaluation of Gabor filtration guidelines. " IJATER, 2, 46-56.

[2] Peng Y. and David D. (2014), "No- guide image quality examination based on aesthetic codebook. " Feature Similarity Index for Image Quality Analysis. " IEEE Trans. IP, 21, 3129-3138.

[3] Zhongyi G. , Lin Z. and Hongyu L. , (2013), "Learning a bling image quality index based on visual saliency guided sampling and Gabor filtering. " ICIP, 186-190.

[4] Lin Z. and Xuanqin M. , (2011), "FSIM: Feature Similarity Index for Image Quality Examination. " IEEE Trans. IP, 20, 2378-2386

[5] Ding Y. , Zhang Y. , Wang X. , Yan X. and Krylov A. S. (2014), "Perceptual image quality metric using common information of Gabor features. " Research China Information Sciences, 57, 032111:1-032111:9.

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