Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/142112
Title: A multi-order derivative feature-based quality assessment model for light field image
Authors: Tian, Yu
Zeng, Huanqiang
Xing, Lu
Chen, Jing
Zhu, Jianqing
Ma, Kai-Kuang
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2018
Source: Tian, Y., Zeng, H., Xing, L., Chen, J., Zhu, J., & Ma, K.-K. (2018). A multi-order derivative feature-based quality assessment model for light field image. Journal of Visual Communication and Image Representation, 57, 212-217. doi:10.1016/j.jvcir.2018.11.005
Journal: Journal of Visual Communication and Image Representation
Abstract: This paper presents an image quality assessment (IQA) model exploring the multi-order derivative feature, called Multi-order Derivative Feature-based Model (MDFM), for evaluating the perceptual quality of light field image (LFI). In our approach, for the input reference and distorted LFIs, the multi-order derivative features are extracted by using the discrete derivative filter to represent the image details in different degrees. Then, the similarities of the extracted derivative features are measured independently. Finally, the weight map is established through the maximum value of the second-order derivative feature of reference and distorted LFIs, which is further utilized to pool the similarity map for generating the final score. Extensive simulation results have demonstrated that the proposed MDFM is more consistent with the perception of the HVS on the evaluation of LFI than the classical and state-of-the-art IQA methods.
URI: https://hdl.handle.net/10356/142112
ISSN: 1047-3203
DOI: 10.1016/j.jvcir.2018.11.005
Rights: © 2018 Elsevier Inc. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

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