A Novel HVS Based Image Contrast Measurement Index

Trivedi, Mridul; Jaiswal, A. K.; Bhateja, Vikrant

doi:10.1007/978-81-322-1000-9_51

Cited by 10 publications

(6 citation statements)

References 13 publications

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“…This slightly high computational load may be neglected as the performance is also enhanced which makes Q reliable for analyzing a variety of distortion types. The proposed metric can be further extended for contrast and sharpness measurement using non-linear operators [56]- [59]. This can be therefore implemented for IQA to evaluate the performance of computer aided analysis systems for breast cancer [60]- [63].…”

Section: Resultsmentioning

confidence: 98%

A Reduced Reference Distortion Measure for Performance Improvement of Smart Cameras

et al. 2015

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Application specific information processing (ASIP) unit in smart cameras requires sophisticated image processing algorithms for image quality improvement and extraction of relevant features for image understanding and machine vision. The improvement in performance as well as robustness can be achieved by intelligent moderation of the parameters both at algorithm (image resolution, contrast, compression, and so on) as well as hardware levels (camera orientation, field of view, and so on). This paper discusses the employment of ISO/IEC/IEEE 21451 smart transducer standards for performance improvement of smart cameras. The standardized transducer electronic data sheets (TEDS-by IEEE 21450) provide the self description of sensors, of which the calibration details are of vital importance to yield a smart and reconfigurable imaging system. This is possible by exercising intelligent control over the TEDS (smart camera) calibration details as well as automated tuning of algorithm parameters (in ASIP) based on decisions by perceptually efficient image quality assessment (IQA) tool. Estimation of distortion based on reduced reference IQA has been highlighted as a reliable methodology for this purpose. The proposed IQA approach uses wavelets for features extraction followed by estimation of luminance, contrast, and divergence parameters to obtain the proposed distortion measure (Q). The computational complexity in the process has been catalyzed using integral image and gradient magnitude approaches. The validation of Q metric is carried out by evaluating the image quality for various types of distortions on images from Content-based Strategies of Image Quality assessment (CSIQ) and Information Visualization CyberInfrastructure (IVC) databases. Simulation results yield a healthy correlation of Q and the subjective human opinions.

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Section: Resultsmentioning

confidence: 98%

A Reduced Reference Distortion Measure for Performance Improvement of Smart Cameras

et al. 2015

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“…Research in finding a meaningful image quality metric is ongoing and several methods have been proposed in Refs. 45, 47–51.…”

Section: Discussionmentioning

confidence: 99%

Multi-frame image fusion using a machine learning-based weight mask predictor for turbulence-induced image degradation

Estrada

Hou²,

Matt

et al. 2023

J. Appl. Rem. Sens.

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.Imaging through degraded visual environments is a challenging task in remote sensing missions. Image degradation may come from the loss of contrast due to particle scattering and/or distortion due to turbulence-induced effects. The problem is especially challenging when imaging from moving platforms such as autonomous underwater vehicles. One potential approach to address these issues is to use multiple images and employ a multi-frame image fusion technique to aid in the recovery of corrupted image detail and quality. A machine learning (ML)-based image enhancement and fusion technique is investigated to restore images distorted by underwater turbulence. The main contributions include the incorporation of a ML-based image weight predictor to predict the ideal weight maps to be used in an image fusion process. This network is trained using a generative adversarial network framework and a synthetically generated image dataset, which is created according to an analytical image degradation model. In addition, the image loss function for the weight map predictor is determined by the final fused image, resulting in a balanced fusion technique that can reduce image distortion, recover crisp image detail, and reduce the overall noise figure. Another key contribution of this paper is the adoption of an image loss function that incorporates an innovative combined correntropy and Fourier space loss function to reinforce the network in both the spatial and frequency domain. The performance of the proposed algorithm is evaluated using a synthetic validation dataset of images and several real datasets captured at the Naval Research Lab’s Simulated Turbulence and Turbidity Environment under various controlled turbulence intensities.

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“…This measure has been so formulated that it could provide evaluation across various distortion types via a single metric in coherence with HVS characteristics [30]. The IQA using this measure has been validated for distortions introduced due to operations like compression, blurring, noise contamination, contrast manipulation.…”

Section: Proposed Iqa Methodologymentioning

confidence: 99%

Image similarity metric (ISIM): a reduced reference image quality assessment approach

Bhateja

Kalsi

Srivastava

2015

CSIT

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Reduced reference image quality assessment (IQA) has been highlighted as a reliable methodology for the purpose of quality-of-service (QoS) monitoring in network visual communication applications; as it requires only selective features of the reference image where complete image cannot be accessed (as reference for IQA). The reduced reference IQA problem has been constrained towards extraction of appropriate features (of reference and transformed images) sensitive to various distortion types along with due coherence with human perception. This paper presents an improved reduced reference IQA approach named as 'ISIM: Image Similarity Metric' based on natural image statistics. The proposed IQA approach involves sub-band decomposition of acquired images for features extraction using wavelets. The extracted features are then used to obtain a global distortion measure (Q) as a function of luminance, contrast and divergence parameters. The computational complexity during features extraction is reduced by employing combination of integral image and gradient magnitude approaches. This distortion measure (Q) is then used to formulate ISIM metric as a linear regression with full-reference SSIM. The validation of the proposed metric is carried out by evaluating the image quality for various types of distortions from CSIQ and IVC databases. Simulation results yield a healthy correlation of proposed ISIM metric and the subjective human opinions along with minimal computational complexity in comparison to other state-of-art reduced reference IQA measures.

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A Novel HVS Based Image Contrast Measurement Index

Cited by 10 publications

References 13 publications

A Reduced Reference Distortion Measure for Performance Improvement of Smart Cameras

A Reduced Reference Distortion Measure for Performance Improvement of Smart Cameras

Multi-frame image fusion using a machine learning-based weight mask predictor for turbulence-induced image degradation

Image similarity metric (ISIM): a reduced reference image quality assessment approach

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