Evaluation of color differences in natural scene color images

Ortiz-Jaramillo, B.; Kumcu, Asli; Platiša, Ljiljana; Philips, Wilfried

doi:10.1016/j.image.2018.11.009

Cited by 20 publications

(20 citation statements)

References 62 publications

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“…Proposed method is based on the evaluation of color differences of pixels. Previous methods for evaluation of color difference are reviewed in [21]. A similar work is proposed in [14], authors have used Canny operator [22] and color vector angles to achieve high value of robustness but there is a limitation of computational time which is approximately twice of the proposed technique.…”

Section: Review Of Image Hashing Shows That Researchers Have Worked Omentioning

confidence: 99%

Robust image hashing based on structural and perceptual features for authentication of color images

Khan¹,

Monir²,

Naseem³

2021

Turk J Elec Eng & Comp Sci

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Image hashing is one of the most celebrated techniques regarding the discipline of image forensics, image retrieval, image indexing, content verification, and zero watermarking. For such sensitive and complex problems, the generation of unique and robust image hash is an utmost prerequisite for an image identifier driven from the perceptual contents of an image. As a design perspective, it is essential for an image hash to have robustness and optimized discriminative capability. We propose a robust image hashing technique by acquiring perceptual features based on a novel distance magnitude profile utilizing color pixel incongruity among the contiguous pixels, as well as producing a structural image for discrimination. The combination of both results provide state-of-the-art robust profile that has efficient discriminative capacity and is impervious to usual signal processing violations.

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Section: Review Of Image Hashing Shows That Researchers Have Worked Omentioning

confidence: 99%

Robust image hashing based on structural and perceptual features for authentication of color images

Khan¹,

Monir²,

Naseem³

2021

Turk J Elec Eng & Comp Sci

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“…As we all know, color plays an extremely important role in our understanding of the world. [32] points out that same color in two blocks can be recognized as the same color only when the color and direction structure information of its adjacent areas are identical. Therefore, there is a certain masking effect between adjacent regions.…”

Section: ) Spatial Maskingmentioning

confidence: 99%

JND-Guided Perceptually Color Image Watermarking in Spatial Domain

et al. 2020

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Watermarking is an effective solution for copyright protection and forensics tracking via hiding information into the image signal. Recently, spatial-embedding watermarking methods from different transform domain have been proposed rapidly and effectively protect the copyright of the color image. Compared to the individual frequency domain method, it has both advantages of spatial domain and frequency domain. Here, we proposed a novel spatial-embedding watermarking method based on an attended just noticeable difference (JND) model with color complexity, to achieve a good tradeoff between robustness and perceptual quality. In particular, at the spatial embedding level, the direct current (DC) coefficients are selected for embedding and the perceptual JND model is used to guide the amount of pixel modification to improve visual quality. Different from the previous JND model, we proposed an attended JND model that considers color complexity, which is more consistent of the human visual perception model. Compared with the other JND models, the proposed JND model is more suitable for the watermarking framework. Experimental results show that the proposed watermarking scheme has better performance than other existing watermarking schemes. This greatly benefits the practical implementations of the spatialembedding watermarking methods.

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“…In addition to this lack of due attention to color-specific distortions is the fact that the quality assessment of a color image is often performed by assessing its luminance component only or its grayscale conversion. As a result, color information in the image is often largely ignored and the precision of assessment results to be influenced accordingly [24]. In particular, although the CQ process is considered a fundamental process for color image analysis and a significant amount of research has been done on CQ, as mentioned above, IQA for CQ degradation has received little attention.…”

Section: Color Quantization Distortionmentioning

confidence: 99%

“…The majority of these databases consider common distortions, i.e. JPEG compression, Gaussian blur, Gaussian white noise, rather than color-specific distortions [24]. In particular, the distortion introduced by the CQ process has been considered initially in Tampere Image Database, namely TID2008 [25].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of quality measures for color quantization

Ramella

2020

Preprint

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Visual quality evaluation is one of the challenging basic problems in image processing. It also plays a central role in the shaping, implementation, optimization, and testing of many methods. The existing image quality assessment methods focused on images corrupted by common degradation types while little attention was paid to color quantization. This in spite there is a wide range of applications requiring color quantization assessment being used as a preprocessing step when color-based tasks are more efficiently accomplished on a reduced number of colors. In this paper, we propose and carry-out a quantitative performance evaluation of nine well-known and commonly used full-reference image quality assessment measures. The evaluation is done by using two publicly available and subjectively rated image quality databases for color quantization degradation and by considering suitable combinations or subparts of them. The results indicate the quality measures that have closer performances in terms of their correlation to the subjective human rating and show that the evaluation of the statistical performance of the quality measures for color quantization is significantly impacted by the selected image quality database while maintaining a similar trend on each database. The detected strong similarity both on individual databases and on databases obtained by integration provides the ability to validate the integration process and to consider the quantitative performance evaluation on each database as an indicator for performance on the other databases. The experimental results are useful to address the choice of suitable quality measures for color quantization and to improve their future employment.

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Evaluation of color differences in natural scene color images

Cited by 20 publications

References 62 publications

Robust image hashing based on structural and perceptual features for authentication of color images

Robust image hashing based on structural and perceptual features for authentication of color images

JND-Guided Perceptually Color Image Watermarking in Spatial Domain

Evaluation of quality measures for color quantization

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