Evaluation of Image Quality Metrics for Color Prints

Pedersen, Marius; Yao, Zheng; Hardeberg, Jon Yngve

doi:10.1007/978-3-642-21227-7_30

Cited by 6 publications

(9 citation statements)

References 21 publications

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“…In the case of printed images, the physical print needs to be digitized prior to QA. Scanners have been used for 2D print digitization [1,2,33,34,37]. Moreover, cameras have been used for QA of flat surfaces [38].…”

Section: Capture Techniquesmentioning

confidence: 99%

“…The SSIM metric is selected because it is a HVS-based FR metric that considers structural information from the image scene [51]. This metric can be used to assess lightness, contrast, sharpness, and artifact attributes [2]. Additionally, Pedersen and Amirshahi [1] mentioned its potential to detect artifacts.…”

Section: Full-reference Image Quality Metricsmentioning

confidence: 99%

“…Objective image Quality Assessment (QA) has a significant demand because it is automatic, consistent, and less resource demanding compared to subjective image QA. There are studies that assess the quality of 2D print images objectively [1,2]. However, 2.5D reproduction QA in an objective way is less studied.…”

Section: Introductionmentioning

confidence: 99%

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Quality Assessment of 2.5D Prints Using 2D Image Quality Metrics

2021

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Quality assessment is an important aspect in a variety of application areas. In this work, the objective quality assessment of 2.5D prints was performed. The work is done on camera captures under both diffuse (single-shot) and directional (multiple-shot) illumination. Current state-of-the-art 2D full-reference image quality metrics were used to predict the quality of 2.5D prints. The results showed that the selected metrics can detect differences between the prints as well as between a print and its 2D reference image. Moreover, the metrics better detected differences in the multiple-shot set-up captures than in the single-shot set-up ones. Although the results are based on a limited number of images, they show existing metrics’ ability to work with 2.5D prints under limited conditions.

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Section: Capture Techniquesmentioning

confidence: 99%

Section: Full-reference Image Quality Metricsmentioning

confidence: 99%

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Quality Assessment of 2.5D Prints Using 2D Image Quality Metrics

2021

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“…They have also incorporated different aspects related to the human visual system, such as contrast sensitivity [19], visual masking [25,26], gaze information [27,28]. These IQMs have been applied to a a wide range of applications, including color printing [29][30][31], displays [32], compression [33,34], cameras [35], image enhancement [36], gamut mapping [37,38], medical imaging [39,40], and biometrics [41][42][43].…”

Section: Introductionmentioning

confidence: 99%

Image Quality Assessment without Reference by Combining Deep Learning-Based Features and Viewing Distance

Chetouani

Pedersen

2021

Applied Sciences

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An abundance of objective image quality metrics have been introduced in the literature. One important essential aspect that perceived image quality is dependent on is the viewing distance from the observer to the image. We introduce in this study a novel image quality metric able to estimate the quality of a given image without reference for different viewing distances between the image and the observer. We first select relevant patches from the image using saliency information. For each patch, a feature vector is extracted from a convolutional neural network model and concatenated at the viewing distance, for which the quality is predicted. The resulting vector is fed to fully connected layers to predict subjective scores for the considered viewing distance. The proposed method was evaluated using the Colourlab Image Database: Image Quality and Viewing Distance-changed Image Database. Both databases provide subjective scores at two different viewing distances. In the Colourlab Image Database: Image Quality we obtain a Pearson correlation of 0.87 at both 50 cm and 100 cm viewing distances, while in the Viewing Distance-changed Image Database we obtained a Pearson correlation of 0.93 and 0.94 at viewing distance of four and six times the image height. The results show the efficiency of our method and its generalization ability.

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“…The assessment of image quality and difference between original and reproduced images has been in the discussion for a number of years [7]. Different algorithms have been proposed and tested for different applications.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the smoothness of color transformations

Aristova

Wang

Hardeberg

2011

Color Imaging XVI: Displaying, Processing, Hardcopy, and Applications

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Multi-dimensional look up tables (LUTs) are widely employed for color transformations due to its high accuracy and general applicability. Using the LUT model generally involves the color measurement of a large number of samples. The precision and uncertainty of the color measurement will be mainly represented in the LUTs, and will affect the smoothness of the color transformation. This, in turn, strongly influences the quality of the reproduced color images. To achieve high quality color image reproduction, the color transformation is required to be relatively smooth. In this study, we have investigated the inherent characteristics of LUTs' transformation from color measurement and their effects on the quality of reproduced images. We propose an algorithm to evaluate the smoothness of 3D LUT based color transformations quantitatively, which is based on the analysis of 3D LUTs transformation from RGB to CIELAB and the second derivative of the differences between adjacent points in vertical and horizontal ramps of each LUT entry. The performance of the proposed algorithm was compared with a those proposed in two recent studies on smoothness, and a better performance is reached by the proposed method.

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Evaluation of Image Quality Metrics for Color Prints

Cited by 6 publications

References 21 publications

Quality Assessment of 2.5D Prints Using 2D Image Quality Metrics

Quality Assessment of 2.5D Prints Using 2D Image Quality Metrics

Image Quality Assessment without Reference by Combining Deep Learning-Based Features and Viewing Distance

Evaluating the smoothness of color transformations

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