Abstract
Automatic assessing the quality of an image is a critical problem for a wide range of applications in the fields of computer vision and image processing. For example, many computer vision applications, such as biometric identification, content retrieval, and object recognition, rely on input images with a specific range of quality. Therefore, an effort has been made to develop image quality assessment (IQA) methods that are able to automatically estimate quality. Among the possible IQA approaches, No-Reference IQA (NR-IQA) methods are of fundamental interest, since they can be used in most real-time multimedia applications. NR-IQA are capable of assessing the quality of an image without using the reference (or pristine) image. In this paper, we investigate the use of texture descriptors in the design of NR-IQA methods. The premise is that visible impairments alter the statistics of texture descriptors, making it possible to estimate quality. To investigate if this premise is valid, we analyze the use of a set of state-of-the-art Local Binary Patterns (LBP) texture descriptors in IQA methods. Particularly, we present a comprehensive review with a detailed description of the considered methods. Additionally, we propose a framework for using texture descriptors in NR-IQA methods. Our experimental results indicate that, although not all texture descriptors are suitable for NR-IQA, many can be used with this purpose achieving a good accuracy performance with the advantage of a low computational complexity.
Highlights
With the fast growth of imaging systems, a large number of digital images are being generated every day
We presented a set of proposed descriptors (MLBP, Multiscale Local Ternary Patterns (MLTP), Local Variance Patterns (LVP), Orthogonal Color Planes Patterns (OCPP), and Salient Local Binary Patterns (SLBP)), which were specially designed for visual quality assessment
We vary the parameters of Local Binary Patterns (LBP), Binarized Statistical Image Features (BSIF), Complete Local Binary Patterns (CLBP), and Local Phase Quantization (LPQ)
Summary
With the fast growth of imaging systems, a large number of digital images are being generated every day. The first consists of performing psychophysical experiments in which humans rate the quality of a set of images These experiments use standardized experimental methodologies to obtain quality scores for a broad range of images processed with a diverse number of algorithms and procedures. Since these experiments use human subjects, this approach is known as subjective quality assessment and it is considered the most accurate method to estimate quality [20]. If a given objective method produces results that are well correlated with the quality scores provided by human viewers, it can be used to replace subjective methods
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