NO-REFERENCE IMAGE QUALITY MEASURE FOR IMAGES WITH MULTIPLE DISTORTIONS USING RANDOM FORESTS FOR MULTI METHOD FUSION Article Swipe

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YOU? · · 2018 · Open Access · · DOI: https://doi.org/10.5566/ias.1534

Over the years image quality assessment is one of the active area of research in image processing. Distortion in images can be caused by various sources like noise, blur, transmission channel errors, compression artifacts etc. Image distortions can occur during the image acquisition process (blur/noise), image compression (ringing and blocking artifacts) or during the transmission process. A single image can be distorted by multiple sources and assessing quality of such images is an extremely challenging task. The human visual system can easily identify image quality in such cases, but for a computer algorithm performing the task of quality assessment is a very difficult. In this paper, we propose a new no-reference image quality assessment for images corrupted by more than one type of distortions. The proposed technique is compared with the best-known framework for image quality assessment for multiply distorted images and standard state of the art Full reference and No-reference image quality assessment techniques available.

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Artificial Intelligence

Enhanced Data Rates For Gsm Evolution

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Concepts

Artificial intelligence Computer science Computer vision Image quality Distortion (music) Ringing Image compression Image (mathematics) Noise (video) Image processing Ringing artifacts Image restoration Image fusion Process (computing) Channel (broadcasting) Pattern recognition (psychology) Enhanced Data Rates for GSM Evolution Operating system Amplifier Computer network Bandwidth (computing)

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Type: article
Language: en
Landing Page: https://doi.org/10.5566/ias.1534
PDF: https://www.ias-iss.org/ojs/IAS/article/download/1534/1068
OA Status: gold
Cited By: 7
References: 50
Related Works: 10
OpenAlex ID: https://openalex.org/W2882747970

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OpenAlex ID: https://openalex.org/W2882747970

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DOI: https://doi.org/10.5566/ias.1534

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Title: NO-REFERENCE IMAGE QUALITY MEASURE FOR IMAGES WITH MULTIPLE DISTORTIONS USING RANDOM FORESTS FOR MULTI METHOD FUSION

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Type: article

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Language: en

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Publication year: 2018

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Publication date: 2018-07-09

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Authors: Kanjar De, V. Masilamani

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Landing page: https://doi.org/10.5566/ias.1534

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PDF URL: https://www.ias-iss.org/ojs/IAS/article/download/1534/1068

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Concepts: Artificial intelligence, Computer science, Computer vision, Image quality, Distortion (music), Ringing, Image compression, Image (mathematics), Noise (video), Image processing, Ringing artifacts, Image restoration, Image fusion, Process (computing), Channel (broadcasting), Pattern recognition (psychology), Enhanced Data Rates for GSM Evolution, Operating system, Amplifier, Computer network, Bandwidth (computing)

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Cited by: 7

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Citations by year (recent): 2025: 2, 2024: 2, 2021: 2, 2020: 1

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