Analysis and Evaluation of Image Quality Metrics

Samajdar, Tina; Quraishi, Md. Iqbal

doi:10.1007/978-81-322-2247-7_38

Cited by 40 publications

(17 citation statements)

References 20 publications

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“…This indicates that UQI is the least similar to the human judgement, which was also reported by Wang et al where he has verified that UQI is an unsuccessful FR-IQA method that fails to correlate with all the subjective assessment [35]. Besides that, UQI is unstable when the mean and standard deviation of the image intensity are close to zero [35,47].…”

Section: Discussionsupporting

confidence: 57%

“…NQM evaluates the image quality based on image degradation model, and it separates the impact of frequency distortion and noise injection on Human Visual System (HVS). It was proven to be an excellent IQA to assess the image quality on natural images [47]. On the other hand, UQI has the lowest mean value of correlation coefficients (PLCC, SROCC and KROCC) and the highest mean value of RMSE with DMOS.…”

Section: Discussionmentioning

confidence: 99%

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Correlation between subjective and objective assessment of magnetic resonance (MR) images

Chow

Rajagopal

Raveendran

2016

Magnetic Resonance Imaging

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Medical Image Quality Assessment (IQA) plays an important role in assisting and evaluating the development of any new hardware, imaging sequences, pre-processing or post-processing algorithms. We have performed a quantitative analysis of the correlation between subjective and objective Full Reference - IQA (FR-IQA) on Magnetic Resonance (MR) images of the human brain, spine, knee and abdomen. We have created a MR image database that consists of 25 original reference images and 750 distorted images. The reference images were distorted with six types of distortions: Rician Noise, Gaussian White Noise, Gaussian Blur, DCT compression, JPEG compression and JPEG2000 compression, at various levels of distortion. Twenty eight subjects were chosen to evaluate the images resulting in a total of 21,700 human evaluations. The raw scores were then converted to Difference Mean Opinion Score (DMOS). Thirteen objective FR-IQA metrics were used to determine the validity of the subjective DMOS. The results indicate a high correlation between the subjective and objective assessment of the MR images. The Noise Quality Measurement (NQM) has the highest correlation with DMOS, where the mean Pearson Linear Correlation Coefficient (PLCC) and Spearman Rank Order Correlation Coefficient (SROCC) are 0.936 and 0.938 respectively. The Universal Quality Index (UQI) has the lowest correlation with DMOS, where the mean PLCC and SROCC are 0.807 and 0.815 respectively. Student's T-test was used to find the difference in performance of FR-IQA across different types of distortion. The superior IQAs tested statistically are UQI for Rician noise images, Visual Information Fidelity (VIF) for Gaussian blur images, NQM for both DCT and JPEG compressed images, Peak Signal-to-Noise Ratio (PSNR) for JPEG2000 compressed images.

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Section: Discussionsupporting

confidence: 57%

Section: Discussionmentioning

confidence: 99%

Correlation between subjective and objective assessment of magnetic resonance (MR) images

Chow

Rajagopal

Raveendran

2016

Magnetic Resonance Imaging

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“…The cycle of denoising utilizing the proposed strategy has appeared in Figure 8. Execution measurements like PSNR, SNR, MSE [15] and SSIM [14] are assessed.…”

Section: Proposed Hybrid Filter Using Obnlm and Dncnnmentioning

confidence: 99%

Untitled

2021

JUSST

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“…IQA techniques could be: Objective or Subjective. Subjective evaluation is timeconsuming, costly and resource-intensive, this type gets along with human visual system (HVS) [9]. Objective image feature metrics can be grouped according to the availability of an original (distortion-free) image, in which the distorted image is to be compared.…”

Section: Objective and Evaluation Metricsmentioning

confidence: 99%

Enhancing Quality of Lossy Compressed Images using Minimum Decreasing Technique

Alshami¹,

Otair²

2018

ijacsa

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The acceleration in technology development came with the urgent need to use large amounts of information, and the way of storing or transferring the huge information via various digital networks became very important issues, particularly in terms of compressing size and quality preservation. The digital image usage has become widespread in many sectors; it become important to manipulate these images to use them in optimal form by maintaining the quality, especially during the compression process using the lossy techniques. This paper presents a new technique to enhance the quality of compressed images while preserving the compression ratio by adding some of pre-processing steps before implementing any existing lossy compression technique. The proposed technique depends on decreasing the minimum elements from the image pixels values in each: row, column, and 2 × 2 block, respectively. These steps minimize the required number of bits to represent each pixel in the compressed images. In order to prove the efficiency of the proposed technique, two lossy compression techniques (novel and classical) were implemented with the proposed. They implemented on wide range of well-known images with different dimensions, sizes, and types. The experimental results show that the quality of the decompressed images with the proposed technique were enhanced in terms of: MSE, MAE and PSNR as quality evaluation metrics.

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Analysis and Evaluation of Image Quality Metrics

Cited by 40 publications

References 20 publications

Correlation between subjective and objective assessment of magnetic resonance (MR) images

Correlation between subjective and objective assessment of magnetic resonance (MR) images

Untitled

Enhancing Quality of Lossy Compressed Images using Minimum Decreasing Technique

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