Evaluation of Blur and Gaussian Noise Degradation in Images Using Statistical Model of Natural Scene and Perceptual Image Quality Measure

Bjelopera, Anamaria; Dumic, Emil; Grgić, Sonja

doi:10.13164/re.2017.0930

Cited by 3 publications

(2 citation statements)

References 12 publications

(21 reference statements)

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“…Unfortunately, such experiments are time-consuming and costly, making the search for alternative quality estimation methods an important research topic. A much simpler approach is to use some computable objective measure that equates quality degradation with the (numerical) error between the original and the distorted media [2,3]. Every objective quality measure has as its aim approximating the human quality perception (or human visual system, HVS) as closely as possible, meaning that good correlation with subjective measures (mean opinion score, MOS) is sought.…”

Section: Introductionmentioning

confidence: 99%

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No-Reference Objective Video Quality Measure for Frame Freezing Degradation

Dumic

Bjelopera

2019

Sensors

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In this paper we present a novel no-reference video quality measure, NR-FFM (no-reference frame–freezing measure), designed to estimate quality degradations caused by frame freezing of streamed video. The performance of the measure was evaluated using 40 degraded video sequences from the laboratory for image and video engineering (LIVE) mobile database. Proposed quality measure can be used in different scenarios such as mobile video transmission by itself or in combination with other quality measures. These two types of applications were presented and studied together with considerations on relevant normalization issues. The results showed promising correlation values between the user assigned quality and the estimated quality scores.

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Section: Introductionmentioning

confidence: 99%

“…NR quality measures require only the processed/degraded signal. RR quality measures need information derived from the original signal [3].…”

Section: Introductionmentioning

confidence: 99%

No-Reference Objective Video Quality Measure for Frame Freezing Degradation

Dumic

Bjelopera

2019

Sensors

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Out of focus multi-spectral image de-blurring using texture extraction and modified fourier transform

Iqbal

Riaz

Ghafoor

et al. 2021

Multimed Tools Appl

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Assessment of Feldkamp-Davis-Kress Reconstruction Parameters in Overall Image Quality in Cone Beam Computed Tomography

Kim,

Choi,

Lee

2024

Applied Sciences

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In low-dose cone beam computed tomography (CT), the insufficient number of photons inevitably results in noise, which reduces the accuracy of disease diagnosis. One approach to improving the image quality of CT images acquired using a low-dose protocol involves the utilization of a reconstruction algorithm that efficiently reduces noise. In this study, we modeled the Feldkamp–Davis–Kress (FDK) algorithm using various filters and projection angles and applied it to the reconstruction process using CT simulation. To quantitatively evaluate the quality of the reconstruction images, we measured the coefficient of variation (COV), and signal-to-noise ratio (SNR) in the air, brain, and bone regions to evaluate the noise level. Furthermore, we calculated root mean square error (RMSE), universal image quality index (UQI), and blind/referenceless image spatial quality evaluator (BRISQUE) as similarity and no-reference evaluation. The Hann filter of the FDK algorithm showed superior performance in terms of COV, SNR, RMSE, and UQI compared to the other filters. In addition, when analyzing the COV and SNR results, we observed that image quality increased significantly at projection angles smaller than approximately 2.8°. Moreover, based on BRISQUE results, we confirm that the Shepp–Logan filter exhibited the most superior performance. In conclusion, we believe that the application of the Hann filter in the FDK reconstruction process offers significant advantages in improving the image quality acquired under a low-dose protocol, and we expect that our study will be a preliminary study of no-reference evaluation of CT reconstruction images.

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Evaluation of Blur and Gaussian Noise Degradation in Images Using Statistical Model of Natural Scene and Perceptual Image Quality Measure

Abstract: Abstract. In this paper we present a new method for clas-

Cited by 3 publications

References 12 publications

No-Reference Objective Video Quality Measure for Frame Freezing Degradation

No-Reference Objective Video Quality Measure for Frame Freezing Degradation

Out of focus multi-spectral image de-blurring using texture extraction and modified fourier transform

Assessment of Feldkamp-Davis-Kress Reconstruction Parameters in Overall Image Quality in Cone Beam Computed Tomography

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