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Neural Computing and Applications

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03.08.2022 | Original Article

Deep belief network for solving the image quality assessment in full reference and no reference model

verfasst von: Dharmalingam Muthusamy, S. Sathyamoorthy

Erschienen in: Neural Computing and Applications | Ausgabe 24/2022

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Abstract

Image Quality Assessment (IQA) is one of the essential problems in image processing. The growth of natural image quality assessment methods has collected a large range of research achievements that are separated into three categories, such as Full Reference Image Quality Assessment (FR-IQA), and Reduced Reference Image Quality Assessment (RR-IQA), and No Reference Image Quality Assessment (NR-IQA). With the rapid growth of digital vision technology, the image quality estimation process quantifies the quality of an image that is used to transmit and acquire images. In this paper, we present a novel Lee Sigma Filterized Mathieu Feature Transformation-based Radial Kernel Deep Belief Network (LSFMFT-RKDBN) model that has been developed for estimating the image quality with and without a reference image. First, the proposed technique performs the quality estimation with full reference called LSFMFT-RKDBN-FR model work that is based on the layer-by-layer method. The visible layer of the Deep Belief Network (DBN) receives the test and reference images. Next, the input test and reference images are de-noised by applying the weighted Lee sigma filter. The de-noised images are partitioned into several patches for accurate feature extraction. Then, the Mathieu transformation is applied to obtain the test feature vector and reference feature vector. At the output layer, the radial basis kernel activation function is applied to analyze the feature vectors and display the estimated results. On the other hand, the proposed model is applied with no reference (LSFMFT-RKDBN-NR) to estimate the test image quality. The LSFMFT-RKDBN-NR model with image de-noising, patch extraction, and feature extraction is carried out to create a test feature vector. Finally, the estimated results are obtained at the output layer. We evaluate the proposed LSFMFT-RKDBN model on the CSIQ Image Quality dataset with qualitative and quantitative results analysis. The proposed LSFMFT-RKDBN model is used to estimate the image quality with higher accuracy and less time and memory consumption when compared to other related methods. The observed result shows the superior performance of the proposed LSFMFT-RKDBN model compared with the two state-of-the-art methods.

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Titel: Deep belief network for solving the image quality assessment in full reference and no reference model
verfasst von: Dharmalingam Muthusamy
S. Sathyamoorthy
Publikationsdatum: 03.08.2022
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 24/2022
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-022-07649-9

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