Referenceless image quality assessment by saliency, color-texture energy, and gradient boosting machines

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.

No-Reference Image Quality Assessment Based on the Fusion of Statistical and Perceptual Features

“…Namely, they pointed out features based on Benford's law are very sensitive to white noise, Gaussian blur, and fast fading. Freitas et al [29] combined the statistics of different color and texture descriptors and mapped onto quality scores using a gradient boosting machine. In an other study, Freitas et al [30] compared the performance of different local binary pattern texture descriptors for NR-IQA.…”