A comparative study of image quality assessment

Preedanan, Wongsakorn; Kondo, Toshiaki; Bunnun, Pished; Kumazawa, Itsuo

doi:10.1109/iwait.2018.8369657

Cited by 20 publications

(11 citation statements)

References 4 publications

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“…Unless stated otherwise, we report the mean squared error (MSE), the structural similarity index (SSIM; Wang, Bovik, Sheikh, & Simoncelli, 2004 ), and either the peak signal to noise ratio (PSNR) or the feature similarity index (FSIM; Zhang, Zhang, Mou, & Zhang, 2011 ) between the reconstruction and the input image, as evaluated using the Scikit-image library (version 0.16.2) for Python ( Van Der Walt et al, 2014 ). Where MSE and PSNR are image quality assessment metrics that operate on pixel intensity, SSIM and FSIM are popular alternatives that better reflect perceptual quality ( Preedanan, Kondo, Bunnun, & Kumazawa, 2018 ). In addition to these performance metrics, we report the average percentage of activated electrodes as a measure for sparsity.…”

Section: Experiments and Resultsmentioning

confidence: 99%

End-to-end optimization of prosthetic vision

et al. 2022

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Neural prosthetics may provide a promising solution to restore visual perception in some forms of blindness. The restored prosthetic percept is rudimentary compared to normal vision and can be optimized with a variety of image preprocessing techniques to maximize relevant information transfer. Extracting the most useful features from a visual scene is a nontrivial task and optimal preprocessing choices strongly depend on the context. Despite rapid advancements in deep learning, research currently faces a difficult challenge in finding a general and automated preprocessing strategy that can be tailored to specific tasks or user requirements. In this paper, we present a novel deep learning approach that explicitly addresses this issue by optimizing the entire process of phosphene generation in an end-to-end fashion. The proposed model is based on a deep auto-encoder architecture and includes a highly adjustable simulation module of prosthetic vision. In computational validation experiments, we show that such an approach is able to automatically find a task-specific stimulation protocol. The results of these proof-of-principle experiments illustrate the potential of end-to-end optimization for prosthetic vision. The presented approach is highly modular and our approach could be extended to automated dynamic optimization of prosthetic vision for everyday tasks, given any specific constraints, accommodating individual requirements of the end-user.

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Section: Experiments and Resultsmentioning

confidence: 99%

End-to-end optimization of prosthetic vision

et al. 2022

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“…-Peak Signal to Noise Ratio (PSNR) Peak signal to noise ratio (PSNR) is a quality measurement calculated based on 2 image objects compared to MSE [21]- [23]. Comparison between the maximum values of the signal is measured by the amount of noise that affects the signal.…”

Section: Discussionmentioning

confidence: 99%

Evaluation the influence of distance-based K-means method for detecting moving vehicles

Sari

Prakoso

Baskara

2022

IOP Conf. Ser.: Mater. Sci. Eng.

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Detecting moving vehicles is one of important elements in the applications of Intelligent Transport System (ITS). Detecting moving vehicles is also part of the detection of moving objects. K-Means method has been successfully applied to unsupervised cluster pixels for the detection of moving objects. In general, K-Means is a heuristic algorithm that partitioned the data set into K clusters by minimizing the number of squared distances in each cluster. In this paper, the K-Means algorithm applies Euclidean distance, Manhattan distance, Canberra distance, Chebyshev distance and Braycurtis distance. The aim of this study is to compare and evaluate the implementation of these distances in the K-Means clustering algorithm. The comparison is done with the basis of K-Means assessed with various evaluation paramaters, namely MSE, PSNR, SSIM and PCQI. The results exhibit that the Manhattan distance delivers the best MSE, PSNR, SSIM and PCQI values compared to other distances. Whereas for data processing time exposes that the Braycurtis distance has more advantages.

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“…Three metrics are used to evaluate the results of image segmentation. They are peak signal-to-noise ratio (PSNR) [21]- [23], structural similarity index (SSIM) [25], and feature similarity index (FSIM) [24]. TABLE 2 is a brief summary of the three parameters.…”

Section: B Pmvo For Multilevel Image Segmentation Problemmentioning

confidence: 99%

“…Since the minimum cross-entropy threshold (MCET) [17]- [20] has the advantage of being able to deal with multilevel threshold constraints very well and obtain accurate threshold values, we apply the proposed PMVO algorithm to optimize the MCET function in order to obtain the thresholds to segment the color image. The segmented image quality is ultimately assessed in three well-known metrics: peak signal noise ratio (PSNR), structural similarity index (SSIM), and feature similarity index (FSIM) [21]- [25]. The results show that the proposed PMVO algorithm can obtain higher quality segmented images than those compared algorithms.…”

Section: Introductionmentioning

confidence: 99%

A Parallel Multi-Verse Optimizer for Application in Multilevel Image Segmentation

2020

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Multi-version optimizer (MVO) inspired by the multi-verse theory is a new optimization algorithm for challenging multiple parameter optimization problems in the real world. In this paper, a novel parallel multi-verse optimizer (PMVO) with the communication strategy is proposed. The parallel mechanism is implemented to randomly divide the initial solutions into several groups, and share the information of different groups after each fixed iteration. This can significantly promote the cooperation individual of MVO algorithm, and reduce the deficiencies that the original MVO is premature convergence, search stagnation and easily trap into local optimal search space. To confirm the performance of the proposed scheme, the PMVO algorithm was compared with the other well-known optimization algorithms, such as gray wolf optimizer (GWO), particle swarm optimization (PSO), multi-version optimizer (MVO), and parallel particle swarm optimization (PPSO) under CEC2013 test suite. The experimental results prove that the PMVO is superior to the other compared algorithms. In addition, PMVO is also applied to solve complex multilevel image segmentation problems based on minimum cross entropy thresholding. The application results appear that the proposed PMVO algorithm can achieve higher quality image segmentation compared to other similar algorithms. INDEX TERMS Meta-heuristic optimization, parallel multi-verse optimizer, multilevel image segmentation, minimum cross entropy thresholding.

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A comparative study of image quality assessment

Cited by 20 publications

References 4 publications

End-to-end optimization of prosthetic vision

End-to-end optimization of prosthetic vision

Evaluation the influence of distance-based K-means method for detecting moving vehicles

A Parallel Multi-Verse Optimizer for Application in Multilevel Image Segmentation

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