Full Reference Image Quality Assessment: A Survey

Phadikar, Baisakhi Sur; Maity, Goutam Kumar; Phadikar, Amit

doi:10.1007/978-981-10-3953-9_19

Cited by 11 publications

(10 citation statements)

References 8 publications

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“…Generally, to perform the used algorithms and evaluate the obtained images in compression, two ways are usable [29]; subjective methods and objective methods. In the subjective context, several measures are developed involving the human observer for assigning a note to a degraded image.…”

Section: Textural Image Quality Evaluation 31 Image Quality Assessmentmentioning

confidence: 99%

Quality and texture analysis of biometric images compressed with second-generation wavelet transforms and SPIHT-Z encoder

Bouida

Beladgham

Bassou

et al. 2020

IJEECS

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<span>In biometric systems, compression takes important place especially in order to reduce the size of the information stored or transmitted through the distributed biometric systems. It is also noted that the compression techniques induce loss of information in the compressed images that can affect the effectiveness of biometric systems. The main objective of our contribution is to examine the efficacy of the used method to offer an optimal compression quality in these kind of images without considerable distortion. In order to evaluate the efficacy of the compression process, we use two kinds of evaluation, full-reference image quality assessment and a new proposed textural quality analysis of the compressed images. In this paper, we use a second-generation wavelet transform to improve the compression study in biometric images. The basic idea of this algorithm is the quincunx wavelet transform coupled to a modified progressive encoder called SPIHT-Z encoding.</span>

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Section: Textural Image Quality Evaluation 31 Image Quality Assessmentmentioning

confidence: 99%

Quality and texture analysis of biometric images compressed with second-generation wavelet transforms and SPIHT-Z encoder

Bouida

Beladgham

Bassou

et al. 2020

IJEECS

View full text Add to dashboard Cite

show abstract

“…With this metric, we calculate the similarity between the original and compressed images on the structural level using the parameters relating to the human vision model like brightness, contrast, and structural composition. However, some research papers present and evaluate other's proposed full-reference image quality assessment like VIF, FSIM, UQI, NQM, VSNR, GSM, IFC, RFSIM [12,19,42].…”

Section: Texture Image Analysismentioning

confidence: 99%

“…Generally and to perform compression algorithms and evaluate the quality of the obtained images, two ways are used [12]. Subjective methods with several measures developed involving the human observer for assigning a note to degraded image and respecting recommendations of ISO 3664-2000 and ITU-BT.500-13.…”

Section: Introductionmentioning

confidence: 99%

“…Objective methods allowing scientists to use numerical image quality assessment (IQA) methods to qualify various image processing applications such as image compression methods. With the reference to the original image, full reference methods (FR-IQA) use the total original image as a reference, reduced reference ones (RR-IQA) use partial reference parameters from the original image and no reference methods (NR-IQA) don't use any reference from the original image [12].…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Textural Degradation in Compressed Medical and Biometric Images by Analyzing Image Texture Features and Edges

Bouida¹,

Beladgham²,

Bassou³

et al. 2020

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The importance of image compression is now essential during transmission or storage processes in various data applications, especially in medical and biometric systems. To perform the effectiveness of the compression process on images and evaluate degradation caused by this process, image quality assessment becomes an important tool in image services. We note that the objective criteria in image quality depend especially on the image type and image texture composition. The actual tendency is to find metrics making better qualification on errors in compressed images and correlate with the human visual system. This paper presents an investigation to examine and evaluate image compression degradation by the use of a new tendency concept of image quality assessment based on texture and edge analysis. To perform and practice this evaluation, we compress the medical and biometric images using second-generation wavelet compression algorithms and study the degradation of texture information in these images.

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“…According to the use of a reference image, the most used objective quality indices are called full reference methods (FR-IQA) because these techniques use the total original image as a reference to compare modification in the compressed image [7]. Resumed from extensive literature sources, hundreds of full-reference image quality assessment, we can categorize the most proposed techniques into seven groups depending on how images are compared [8,9]:  Pixel difference-based measures exploiting direct features from the pixels of compared images such as energy difference or mean square distortion;…”

Section: Introductionmentioning

confidence: 99%

Monte Carlo Optimization of a Combined Image Quality Assessment for Compressed Images Evaluation

Bouida¹,

Khelifi²,

Beladgham³

et al. 2021

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In image processing, using compression is very important in various applications, especially those using data quantities in transmission and storing. This importance becomes most required with the evolution of image quantities and the big data systems explosion. The image compression allows reducing the required binary volume of image data by encoding the image for transmission goal or database saving. The principal problem with image compression when reducing its size is the degradation that enters the image. This degradation can affect the quality of use of the compressed image. To evaluate and qualify this quality, we investigate the use of textural combined image quality metrics (TCQ) based on the fusion of full reference structural, textural, and edge evaluation metrics. To optimize this metric, we use the Monte Carlo optimization method. This approach allows us to qualify our compressed images and propose the best metric that evaluates compressed images according to several textural quality aspects.

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Full Reference Image Quality Assessment: A Survey

Cited by 11 publications

References 8 publications

Quality and texture analysis of biometric images compressed with second-generation wavelet transforms and SPIHT-Z encoder

Quality and texture analysis of biometric images compressed with second-generation wavelet transforms and SPIHT-Z encoder

Evaluation of Textural Degradation in Compressed Medical and Biometric Images by Analyzing Image Texture Features and Edges

Monte Carlo Optimization of a Combined Image Quality Assessment for Compressed Images Evaluation

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