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Blind Image Quality Assessment via Vector Regression and Object Oriented Pooling
Jul 10, 2018Author:
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Title: Blind Image Quality Assessment via Vector Regression and Object Oriented Pooling

Authors: Gu, J; Meng, GF; Redi, JA; Xiang, SM; Pan, CH

Author Full Names: Gu, Jie; Meng, Gaofeng; Redi, Judith A.; Xiang, Shiming; Pan, Chunhong

Source: IEEE TRANSACTIONS ON MULTIMEDIA, 20 (5):1140-1153; 10.1109/TMM.2017.2761993 MAY 2018

Language: English

Abstract: This paper presents an effective method based on vector regression and object oriented pooling for blind image quality assessment. Unlike previous models that map the extracted features directly to a quality score, the proposed vector regression framework yields a vector of belief scores for the input image. We explore the uncertainty factors in quality assessment and design the belief scores to measure the confidences of an image to be assigned to the corresponding quality grades. Moreover, we propose an object oriented pooling strategy to further improve the performance by incorporating semantic information of image contents. According to this strategy, regions occupied by objects will be assigned more weights in the pooling phase, leading to a more accurate quality assessment. Extensive experiments on benchmark datasets demonstrate that our approach achieves state-of-the-art performance and shows a great generalization ability.

ISSN: 1520-9210

eISSN: 1941-0077

IDS Number: GD7YD

Unique ID: WOS:000430728400010

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