Due to the rapid development of multimedia technologies, over the last decades image quality assessment (IQA) has become an important topic. As a consequence, a great research effort has been made to develop computational models that estimate image quality. Among the possible IQA approaches, blind IQA (BIQA) is of fundamental interest as it can be used in most multimedia applications. BIQA techniques measure the perceptual quality of an image without using the reference (or pristine) image. This paper proposes a new BIQA method that uses a combination of texture features and saliency maps of an image. Texture features are extracted from the images using the local binary pattern (LBP) operator at multiple scales. To extract the salient of an image, i.e. the areas of the image that are the main attractors of the viewers' attention, we use computational visual attention models that output saliency maps. These saliency maps can be used as weighting functions for the LBP maps at multiple scales. We propose an operator that produces a combination of multiscale LBP maps and saliency maps, which is called the multiscale salient local binary pattern (MSLBP) operator. To define which is the best model to be used in the proposed operator, we investigate the performance of several saliency models. Experimental results demonstrate that the proposed method is able to estimate the quality of impaired images with a wide variety of distortions. The proposed metric has a better prediction accuracy than state-of-the-art IQA methods.

This paper proposes a new general-purpose no-reference image quality assessment (NR-IQA) method based on color texture analysis. Specifically, the proposed method uses the statistics of the orthogonal color planes pattern (OCPP) descriptor to characterize image quality. The OCPP descriptor, proposed in this paper, is an extension of the local binary pattern operator that incorporates color information. To make NR-IQA methods more generic, that is, more sensitivity to different types of degradation (e.g., color and contrast degradation), it is important to take into consideration the color information. In the proposed NR-IQA method, we use the statistics of the OCPP descriptor as an input vector to a regression algorithm, which models the nonlinear relationship between the OCPP statistics and the subjective opinion scores. Experimental results show that proposed IQA method is quite efficient when compared to popular state-of-the-art NR-IQA methods.

In this paper, we propose a new no-reference image quality assessment (NR-IQA) method that uses a machine learning technique based on Local Ternary Pattern (LTP) descriptors. LTP descriptors are a generalization of Local Binary Pattern (LBP) texture descriptors that provide a significant performance improvement when compared to LBP. More specifically, LTP is less susceptible to noise in uniform regions, but no longer rigidly invariant to gray-level transformation. Due to its insensitivity to noise, LTP descriptors are not able to detect milder image degradation. To tackle this issue, we propose a strategy that uses multiple LTP channels to extract texture information. The prediction algorithm uses the histograms of these LTP channels as features for the training procedure. The proposed method is able to blindly predict image quality, i.e., the method is no-reference (NR). Results show that the proposed method is considerably faster than other state-of-the-art no-reference methods, while maintaining a competitive image quality prediction accuracy.