Entropy Based Image Identifier Generation

Park, Je-Ho; Kang, Changwon

doi:10.1109/icitcs.2014.7021766

Cited by 1 publication

(3 citation statements)

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“…The identification of enemy targets in synthetic aperture radar pictures are discussed in [11] which elaborates with more accuracy with binary image matching method for estimation of the clear target. The author of [19] increased the rate of identification of picture through unique identifier generation, which is the fusion of entropy with features of picture. For comparing the quality of super resolution picture with reference picture RMSE, MSE, PSNR are used in [21] and MSE,PSNR, EnT and Q-INDEX are used in [22].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Entropy(EnT) is a statistical calculation for arbitrariness [19] [20] in characterizing input picture surface. EnT is characterized by (10) as…”

Section: Evaluation Of Picture Contentsmentioning

confidence: 99%

“…Then it attempted to co-relate objectively by using mean squared error (MSE) [9], root mean squared error (RMSE) [9] and peak signal to noise ratio(PSNR) [9]. Later the details of picture are weighed through statistical measure of haphazardness that can be utilized to portray the composition of the picture by means of entropy(EnT) [19][20].…”

Section: Introductionmentioning

confidence: 99%

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Data Content Weighing for Subjective versus Objective Picture Quality Assessment of Natural Pictures

Suresha¹,

Prakash²

2017

IJIGSP

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Abstract-Estimating the visual quality of picture is a real challenge for various picture and video frame applications. The aim is to evaluate the quality of picture automatically in both subjective (human visual frame work) and objectively. The quality of picture is evaluated by comparing precision and closeness of a picture with reference or error free picture. The quality estimation can be done to achieve consistency in desired quality of picture with help of modeling remarkable physiological, psycho visual components framework and picture fidelity measure methods. In this article, the picture quality is evaluated by analyzing loss of picture information of the distortion system using differing noise models and examine the relationship between picture data, visual quality and error metric. The quality of picture & video frame assessment is really important that, every human can judge the visual quality of natural picture. The subjective quality of picture is assessed by using structural similarity metric, objective quality of picture is computed by root means squared error, mean squared error and peak signal to noise ratio and data content in picture is weighted through entropy.

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Section: Literature Reviewmentioning

confidence: 99%

“…Entropy(EnT) is a statistical calculation for arbitrariness [19] [20] in characterizing input picture surface. EnT is characterized by (10) as…”

Section: Evaluation Of Picture Contentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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