Image Complexity Measure: a Human Criterion Free Approach

Mario, I.; Chacon, M.I.; Alma, D.; Corral, S.

doi:10.1109/nafips.2005.1548541

Cited by 32 publications

(12 citation statements)

References 7 publications

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“…Some works have related image complexity to entropy of image intensity [15]. In [16], complexity has been considered as a subjective characteristic that is represented by a fuzzy interpretation of edges in an image. In addition, there are some new definitions of image complexity but these approaches are highly application dependent [17,18].…”

Section: Complexity Measuresmentioning

confidence: 99%

Estimating Watermarking Capacity in Gray Scale Images Based on Image Complexity

Yaghmaee

Jamzad

2010

EURASIP J. Adv. Signal Process.

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Capacity is one of the most important parameters in image watermarking. Different works have been done on this subject with different assumptions on image and communication channel. However, there is not a global agreement to estimate watermarking capacity. In this paper, we suggest a method to find the capacity of images based on their complexities. We propose a new method to estimate image complexity based on the concept of Region Of Interest (ROI). Our experiments on 2000 images showed that the proposed measure has the best adoption with watermarking capacity in comparison with other complexity measures. In addition, we propose a new method to calculate capacity using proposed image complexity measure. Our proposed capacity estimation method shows better robustness and image quality in comparison with recent works in this field.

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Section: Complexity Measuresmentioning

confidence: 99%

Estimating Watermarking Capacity in Gray Scale Images Based on Image Complexity

Yaghmaee

Jamzad

2010

EURASIP J. Adv. Signal Process.

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“…This high level property is useful in many real applications of image processing, such as feature extraction, image watermarking and feature mining (Iliyasu et al, 2013b). Visual complexity has been represented using single features such as quad tree (Yaghmaee and Jamzad, 2005), edge based measurement (Mario et al, 2005), fuzzy measures of entropy (Cardici et al, 2009) and entropy based information (Rigau et al, 2005). Figure 5 shows a simple case of varying visual complexity of three images (increasing from left to right), one each from the flower, food and building categories of the Corel 1000A database (Griffin et al, 2013).…”

Section: Snd Space For Representing Image Visual Complexitymentioning

confidence: 99%

A Visual Complexity-sensitive DWT Ordering Scheme for Hiding Data in Images

Iliyasu¹,

Al-Asmari²,

Salama³

et al. 2014

RJASET

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A scheme is proposed to hide data in images based on a prioritized ordering of the content of the host (or cover) image. A watermark embedding process uses the watermark strength to determine the ordering of the 16 regions resulting from the second level Discrete Wavelet Transform (DWT) decomposed content of the host image. To determine the best ordering for hiding the data, various types of image of varying content and, hence, visual complexity were considered, analyzed and ranked in terms of their ability to withstand changes that do not imperil the visual quality (PSNR) of their watermarked versions depending on which an N×N-sized watermark stream is hidden in the 8 highest ranked sub-bands of the host image. From this perspective, an ordering for images classified as simple, normal and complex images was used to determine a generalized ordering of the DWT decomposed sub-bands of the image. The generalized ordering presented here ascertains that the content of the image and its visual complexity had little effect on an earlier proposed prioritized ordering of the DWT Sub-bands. To validate the veracity of the ordering scheme, 1000 images from the Corel 1000A database, their visual complexity and features were taken into account. The results confirmed that high embedding capacity, appreciable visual quality of watermarked images and complete recovery hidden data are realizable based on the ordering scheme proposed in this study.

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“…Measuring image complexity has been extensively studied in the literature [28,29]. The unique difficulty our approach faces, however, is that the unknown subregions are heavily occluded by the foreground object, thus directly computing features from them is inapplicable.…”

Section: Background-complexity-based Featuresmentioning

confidence: 99%