IEEE Trans Image Process - Blind image quality assessment using joint statistics of gradient magnitude and Laplacian features.

Tópicos

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Resumo

Blind image quality assessment (BIQA) aims to evaluate the perceptual quality of a distorted image without information regarding its reference image. Existing BIQA models usually predict the image quality by analyzing the image statistics in some transformed domain, e.g., in the discrete cosine transform domain or wavelet domain. Though great progress has been made in recent years, BIQA is still a very challenging task due to the lack of a reference image. Considering that image local contrast features convey important structural information that is closely related to image perceptual quality, we propose a novel BIQA model that utilizes the joint statistics of two types of commonly used local contrast features: 1) the gradient magnitude (GM) map and 2) the Laplacian of Gaussian (LOG) response. We employ an adaptive procedure to jointly normalize the GM and LOG features, and show that the joint statistics of normalized GM and LOG features have desirable properties for the BIQA task. The proposed model is extensively evaluated on three large-scale benchmark databases, and shown to deliver highly competitive performance with state-of-the-art BIQA models, as well as with some well-known full reference image quality assessment models.

Resumo Limpo

blind imag qualiti assess biqa aim evalu perceptu qualiti distort imag without inform regard refer imag exist biqa model usual predict imag qualiti analyz imag statist transform domain eg discret cosin transform domain wavelet domain though great progress made recent year biqa still challeng task due lack refer imag consid imag local contrast featur convey import structur inform close relat imag perceptu qualiti propos novel biqa model util joint statist two type common use local contrast featur gradient magnitud gm map laplacian gaussian log respons employ adapt procedur joint normal gm log featur show joint statist normal gm log featur desir properti biqa task propos model extens evalu three largescal benchmark databas shown deliv high competit perform stateoftheart biqa model well wellknown full refer imag qualiti assess model

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