Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/140030
Title: Optimizing multistage discriminative dictionaries for blind image quality assessment
Authors: Jiang, Qiuping
Shao, Feng
Lin, Weisi
Gu, Ke
Jiang, Gangyi
Sun, Huifang
Keywords: Engineering::Computer science and engineering
Issue Date: 2017
Source: Jiang, Q., Shan, F., Lin, W., Gu, K., Jiang, G., & Sun, H. (2018). Optimizing multistage discriminative dictionaries for blind image quality assessment. IEEE Transactions on Multimedia, 20(8), 2035-2048. doi:10.1109/TMM.2017.2763321
Journal: IEEE Transactions on Multimedia
Abstract: State-of-the-art algorithms for blind image quality assessment (BIQA) typically have two categories. The first category approaches extract natural scene statistics (NSS) as features based on the statistical regularity of natural images. The second category approaches extract features by feature encoding with respect to a learned codebook. However, several problems need to be addressed in existing codebook-based BIQA methods. First, the high-dimensional codebook-based features are memory-consuming and have the risk of over-fitting. Second, there is a semantic gap between the constructed codebook by unsupervised learning and image quality. To address these problems, we propose a novel codebook-based BIQA method by optimizing multistage discriminative dictionaries (MSDDs). To be specific, MSDDs are learned by performing the label consistent K-SVD (LC-KSVD) algorithm in a stage-by-stage manner. For each stage, a new quality consistency constraint called 'quality-discriminative regularization' term is introduced and incorporated into the reconstruction error term to form a unified objective function, which can be effectively solved by LC-KSVD for discriminative dictionary learning. Then, the latter stage takes the reconstruction residual data in the former stage as input based on which LC-KSVD is repeatedly performed until the final stage is reached. Once the MSDDs are learned, multistage feature encoding is performed to extract feature codes. Finally, the feature codes are concatenated across all stages and aggregated over the entire image for quality prediction via regression. The proposed method has been evaluated on five databases and experimental results well confirm its superiority over existing relevant BIQA methods.
URI: https://hdl.handle.net/10356/140030
ISSN: 1520-9210
DOI: 10.1109/TMM.2017.2763321
Rights: © 2017 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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