Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/142299
Title: Unified no-reference quality assessment of singly and multiply distorted stereoscopic images
Authors: Jiang, Qiuping
Shao, Feng
Gao, Wei
Chen, Zhuo
Jiang, Gangyi
Ho, Yo-Sung
Keywords: Engineering::Computer science and engineering
Issue Date: 2018
Source: Jiang, Q., Shao, F., Gao, W., Chen, Z., Jiang, G., & Ho, Y.-S. (2019). Unified no-reference quality assessment of singly and multiply distorted stereoscopic images. IEEE Transactions on Image Processing, 28(4), 1866-1881. doi:10.1109/TIP.2018.2881828
Journal: IEEE Transactions on Image Processing
Abstract: A challenging problem in the no-reference quality assessment of multiply distorted stereoscopic images (MDSIs) is to simulate the monocular and binocular visual properties under a mixed type of distortions. Due to the joint effects of multiple distortions in MDSIs, the underlying monocular and binocular visual mechanisms have different manifestations with those of singly distorted stereoscopic images (SDSIs). This paper presents a unified no-reference quality evaluator for SDSIs and MDSIs by learning monocular and binocular local visual primitives (MB-LVPs). The main idea is to learn MB-LVPs to characterize the local receptive field properties of the visual cortex in response to SDSIs and MDSIs. Furthermore, we also consider that the learning of primitives should be performed in a task-driven manner. For this, two penalty terms including reconstruction error and quality inconsistency are jointly minimized within a supervised dictionary learning framework, generating a set of quality-oriented MB-LVPs for each single and multiple distortion modality. Given an input stereoscopic image, feature encoding is performed using the learned MB-LVPs as codebooks, resulting in the corresponding monocular and binocular responses. Finally, responses across all the modalities are fused with probabilistic weights which are determined by the modality-specific sparse reconstruction errors, yielding the final monocular and binocular features for quality regression. The superiority of our method has been verified on several SDSI and MDSI databases.
URI: https://hdl.handle.net/10356/142299
ISSN: 1057-7149
DOI: 10.1109/TIP.2018.2881828
Rights: © 2018 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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