Abstract
In most practical multimedia applications, processes are used to manipulate the image content. These processes include compression, transmission, or restoration techniques, which often create distortions that may be visible to human subjects. The design of algorithms that can estimate the visual similarity between a distorted image and its non-distorted version, as perceived by a human viewer, can lead to significant improvements in these processes. Therefore, over the last decades, researchers have been developing quality metrics (i.e., algorithms) that estimate the quality of images in multimedia applications. These metrics can make use of either the full pristine content (full-reference metrics) or only of the distorted image (referenceless metric). This paper introduces a novel referenceless image quality assessment (RIQA) metric, which provides significant improvements when compared to other state-of-the-art methods. The proposed method combines statistics of the opposite color local variance pattern (OC-LVP) descriptor with statistics of the opposite color local salient pattern (OC-LSP) descriptor. Both OC-LVP and OC-LSP descriptors, which are proposed in this paper, are extensions of the opposite color local binary pattern (OC-LBP) operator. Statistics of these operators generate features that are mapped into subjective quality scores using a machine-learning approach. Specifically, to fit a predictive model, features are used as input to a gradient boosting machine (GBM). Results show that the proposed method is robust and accurate, outperforming other state-of-the-art RIQA methods.
Highlights
The rapid growth of current multimedia industry, and the consequent increase in content quality requirements, have prompted the interest in visual quality assessment methodologies [1]
We introduce a NDS-general-purpose referenceless image quality assessment (RIQA) methods (GP-RIQA) method based on machine learning (ML) that tackles these limitations by taking into account how impairments affect salient color-texture and energy information
In this paper, we proposed a novel NDS-GP-RIQA method based on the statistics of two new texture descriptors: the opposite color local salient pattern (OC-local salient pattern (LSP)) and OC-local variant patterns (LVP)
Summary
The rapid growth of current multimedia industry, and the consequent increase in content quality requirements, have prompted the interest in visual quality assessment methodologies [1]. Some works proposed feature extraction used texture information to estimate image quality [19,20,21,22,23,24,25,26,27]. The acclaimed structural similarity index (SSIM) [32] is based on the assumption that HVS is more sensitive to the structural information of the visual content and, a structural similarity measure can provide a good estimate of the perceived image quality. HVS-based image quality approaches that incorporate visual saliency models (VSM) have been a trend [40,41,42,43]. Image quality metrics and VSM are inherently correlated because both of them take into account how the HVS perceives the visual content (i.e., how humans perceive suprathreshold distortions) [42].
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