期刊
PROCEEDINGS OF THE 9TH ACM MULTIMEDIA SYSTEMS CONFERENCE (MMSYS'18)
卷 -, 期 -, 页码 52-63出版社
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3204949.3204960
关键词
Quality Metrics; Performance Assessment; Blind Image Quality Assessment; Quality of Experience
资金
- Conselho Nacional de Desenvolvimento Cientfico e Tecnologico (CNPq)
- Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES)
- Fundacao de Apoio a Pesquisa do Distrito Federal (FAP-DF)
- University of Brasilia (UnB)
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 areas 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.
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