Window size influence on SSIM fidelity

Golestani, Hossein Bakhshi; Ghanbari, M.

doi:10.1109/istel.2014.7000728

Cited by 7 publications

(5 citation statements)

References 9 publications

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“…In summary, a review of the literature on SSIM suggests the following implications for this research: First, if one specifies a window and measures the SSIM at each point [15], the SSIM can detect changes in different parts of the video. If there is a significant change between what the projector is trying to show and what one sees, it can be assumed that something is distorting the image.…”

Section: Related Research 21 Structural Similarity Index Measurementioning

confidence: 99%

“…The smaller window size would derive the more sensitive SSIM value. However, it should be at least 3 × 3 [15]. If the window is too large, there may not be any difference in SSIM values based on the location of the image [15].…”

Section: Ssim-map Generatormentioning

confidence: 99%

“…However, it should be at least 3 × 3 [15]. If the window is too large, there may not be any difference in SSIM values based on the location of the image [15]. As mentioned before, the proposed system generates two different SSIM-MAPs, called the Channel SSIM-MAP and the Time SSIM-MAP, and then merges them into a single SSIM-MAP to be used for human area segmentation by the Brightness Control Area Refiner.…”

Section: Ssim-map Generatormentioning

confidence: 99%

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A Regional Brightness Control Method for a Beam Projector to Avoid Human Glare

Jeon,

Lee

2024

Applied Sciences

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In this study, we proposed a system to reduce the speaker’s suffering from the strong light of a beam projector by applying regional brightness control over the screen. Since the original image and the projected one on the screen are quite different in area, brightness, and color, the proposed system first transforms them so that they have the same area and similar color tone. Then, to accurately determine the difference between those images, we have introduced a SSIM map, which is a perception-based method of measuring image similarity. Accordingly, an image segmentation model is used to determine the speaker’s silhouette from the SSIM map. We applied a couple of well-trained segmentation models, such as Selfie and DeepLab-v3, provided with MediaPipe. The experimental results showed the operability of the proposed system and that it determines most of a lecturer’s body area on the screen. To closely evaluate the system’s effectiveness, we have measured error rates consisting of false-positive and false-negative errors in the confusion matrix. With the measured results, the error rates appeared so insignificant and stable that the proposed system provides a practical effect for the speakers, especially in the case of applying DeepLab-v3. With the results, it is implied that an accurate segmentation model can considerably elevate the effectiveness of the system.

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Section: Related Research 21 Structural Similarity Index Measurementioning

confidence: 99%

Section: Ssim-map Generatormentioning

confidence: 99%

Section: Ssim-map Generatormentioning

confidence: 99%

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A Regional Brightness Control Method for a Beam Projector to Avoid Human Glare

Jeon,

Lee

2024

Applied Sciences

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“…In experiments, Guo et al [66] used fMSE as the main metric. Guo et al [29] believed that MSE and SSIM measure the average error of all pixels in the whole dataset and are not suitable for background analysis, so use a small window size (11 × 11) [98] for SSIM and fSSIM to avoid unrealistic results due to different foreground sizes. Similarity reflection for harmonization.…”

Section: Evaluation Metrics 51 Traditional Evaluation Metricsmentioning

confidence: 99%

The Way to picture Reality: A Review, Evaluation and Recipe of Image Harmonization

Tian

Hou

Zhang

2023

Preprint

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Image harmonization is an interesting but challenging task in image processing. The task is to adjust the visual information between the foreground and background in the picture, so that the overall picture becomes a harmonious and unified process. Compared to traditional unsupervised methods for image processing, recent developments in deep learning have brought new opportunities to the field. Since 2019, related image harmonization papers have begun to appear on a large scale. In order to better sort out the development of this field, we summarize the field from two aspects of supervised learning and unsupervised learning, and also deeply integrate the existing image harmonization dataset and related index evaluation. We then conduct a detailed comparison and analysis of the existing models. Finally, the current challenges and future research directions of deep learning in image harmonization are discussed.

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“…24 pixels) sliding window and calculate the SSIM as the average over all local SSIM results. It shall be noted that the window size influences the results and needs to be adjusted with care [87,88]. Values of the SSIM range between −1 and 1; however, negative values can only be obtained if the volumes are negatively correlated, which is typically not the case and in particular not for our application.…”

Section: Image Quality Metrics For Reconstruction Evaluationmentioning

confidence: 99%

Selection and evaluation of spherical acquisition trajectories for industrial computed tomography

2021

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In conventional industrial computed tomography, the source–detector system rotates in equiangular steps in-plane relative to the part of investigation. While being by far the most frequently used acquisition trajectory today, this method has several drawbacks like the formation of cone beam artefacts or limited usability in case of geometrical restrictions. In such cases, the usage of alternative spherical trajectories can be beneficial to improve image quality and defect visibility. While investigations have been performed to relate the influence of the trajectory choice in the typical metrological case of a high number of available projections, so far barely any work has been done for the case of few source–detector poses, which is more relevant in the field of non-destructive testing. In this work, we provide an overview of quantitative metrics that can be used to assess the image quality of reconstructed computed tomography volumes, discuss their advantages and drawbacks and propose a framework to investigate the performance of several non-standard trajectories with respect to previously defined regions of interest. Inspired by pseudorandom sampling methods for Monte–Carlo-algorithms, we also suggest an entirely new trajectory design, the low-discrepancy spherical trajectory, which extends the concept of equiangular planar trajectories into three dimensions and can be used for benchmarking and comparison with other spherical trajectories. Last, we use an optimization method to calculate task-specific acquisition trajectories and relate their performance to other spherical designs.

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Window size influence on SSIM fidelity

Cited by 7 publications

References 9 publications

A Regional Brightness Control Method for a Beam Projector to Avoid Human Glare

A Regional Brightness Control Method for a Beam Projector to Avoid Human Glare

The Way to picture Reality: A Review, Evaluation and Recipe of Image Harmonization

Selection and evaluation of spherical acquisition trajectories for industrial computed tomography

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