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ABSTRACT - The robustness of watermarks to geometric

ABSTRACT - The robustness of watermarks to geometric

extension to colour images is usually accomplished by marking the image luminance component or by processing each colour channel separately [3, 4]. Kutter et al. [5] proposed an alternative method for watermarking colour images. It is based on embedding a watermark by modifying a selected set of pixel values in the blue channel, since the human eye is less sensitive to changes in this band. To achieve robustness against JPEG compression attack, Lian et al. [6] suggested that the watermark should be embedded into the green component rather than the red or blue components of the colour image. This is because the loss of energy of the blue and red components is higher than the green component when the watermarked image is attacked by JPEG compression. However, the human eye is more sensitive to changes in the green band. In Lian’s method, the watermark is embedded into the largest coefficients of the low- frequency subband of the DWT. The watermark capacity is limited, since the watermark is embedded into only the significant coefficient of transformed image, which mostly contains only a few significant coefficients. Fleet and Heeger [7] proposed a method, which takes into account the characteristics of the human visual system (HVS) with respect to colour perception. They suggested embedding the watermark into the yellow-blue channel of colour images and using the S-CIELAB space to measure the color reproduction error. However, their method can only resist printing and rescanning attacks. Barni et al. [8] introduced another colour image watermarking method based on the cross-correlation of RGB channels. However, it has relative high computing costs and low processing speed since the full-frame DCT is used for three colour channels. Tsai et al. [9] provided a solution of embedding the watermark on a quantized colour image. Kutter at al. [10] investigated watermarking of luminance and blue- channels using a perceptual model, which takes into account the sensitivity and the masking behaviour of the HVS. Huang et al. [11] embedded the watermark into DC coefficients of a colour image directly in the spatial domain, followed by a saturation adjustment technique performed in the RGB colour space. Nasir et al. [12] suggested encoding the watermark by using convolution coding and then embedding multiple watermarks into the blue channel of the host color image. However, the detection process in [11,12 ] required the original images, which may not be available in some application. In [13] the watermark is embedded in luminance component of the color image using discrete wavelet domain. Authors in [14] suggested extracting the watermark by training support vector machine. However, robustness against geometric attack was not addressed by these methods.
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Comparative Efficiency of Color Models for Multi-focus Color Image Fusion

Comparative Efficiency of Color Models for Multi-focus Color Image Fusion

Abstract — The comparative efficiency of color models for multi-focus color image fusion is presented in this paper. The objective of these experiments is to finding the proper color model for using in multi-focus color image fusion. In our research study, firstly we transform RGB color model of source images into four color models that are YIQ, YCbCr, HSV and HSI color models. Next, the intensity or luminance component is only used in fusion process using Spatial Frequency Measurement based fusion method compared with Stationary Wavelet Transform with Extended Spatial Frequency Measurement. Finally, the fused image results are transformed back to RGB model to get the final results. The experiments show that the YCbCr color model outperforms other color models in term of objective quality assessment.
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The Which Blair Project : A Quick Visual Method for Evaluating Perceptual Color Maps

The Which Blair Project : A Quick Visual Method for Evaluating Perceptual Color Maps

In the analysis so far, we have discussed the rating data in terms of the monotonicity of the luminance profiles for each of the color scales. We saw a general decrease in rating score for higher luminance color maps, suggesting the existence of an underlying contrast transducer function. To test this hypothesis, Figure 8 depicts the percentage of favorable ratings (“1” or “2”) as a function of luminance contrast for all 63-color scales. We have taken L* contrast as our measure, but the graph is essentially indistinguishable if we use L, the normalized luminance values. The black symbols represent color scales with a monotonic luminance component over their entire range. The gray symbols indicate color scales with no luminance contrast. The open symbols indicate color scales with non- monotonic or decreasing luminance components. The symbol shapes indicate the color scales from which these data were drawn.
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Content Based Image Retrieval Using Color, Texture and Hybrid Features

Content Based Image Retrieval Using Color, Texture and Hybrid Features

1) Color Histogram: The main method of representing color information of images in CBIR systems is through color histograms. The color histogram is a method for describing the color content of an image; it counts the number of occurrences of each color in an image. A color histogram H for a given image is defined as a vector H = {h[1], h[2],.. h[i],.., h[N]} where i represents a color in the color histogram, h[i] is the number of pixels in color i in that image, and N is the number of bins in the color histogram, i.e., the number of colors in the adopted color model. A color histogram is a type of bar graph, where each bar represents a particular color of the color space being used. The bars in a color histogram are referred to as bins and they represent the x-axis. The number of bins depends on the number of colors there are in an image. The y-axis denotes the number of pixels there are in each bin. For the purpose of saving time, color quantization is applied (H=8, S=2, V=2 levels). As a color space, HSV (Hue, Saturation, and Value) space is used. HSV color space is found to be more suitable in case of plotting a histogram since it separates the color components (HS) from the luminance component (V) and is less sensitive to illumination changes [18].
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Copyright Protection using Video Watermarking based on Wavelet Transformation in Multiband

Copyright Protection using Video Watermarking based on Wavelet Transformation in Multiband

In this paper, a new video watermarking scheme which is based on DWT in multiband to develop a new non-blind scheme that is resistant to a variety of attacks. The watermark is embedded in the luminance component of each frame of the original video because the luminance component as comparison chrominance components is less sensitive to human eye. Why DWT is chosen? It is best described in section II. In our approach the lowest (LL) and the highest (HH) frequency bands are selected to apply wavelet transformation to embed the watermark. Video frames are taken as the input, and watermark is embedded in each frame by altering the wavelet coefficients of selected DWT sub bands.
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Research on Algorithms of SAR and Optical Image Fusion

Research on Algorithms of SAR and Optical Image Fusion

In recent years, HIS-based image fusion methods have gradually become a hot topic in the field of SAR image fusion. The HIS transform method can well add the spatial structure and feature information of the SAR image to the visible light image, but it has the phenomenon of spectral distortion caused by the addition. At present, there are many feature addition methods that overcome severe spectral distortion. For example, based on the method of luminance component modulation, the texture feature of the SAR image is extracted by using the ratio of the SAR image after filtering to the low-pass approximation image pixel value, and is used to modulate the luminance component of the multi-spectral image added to the panchromatic image spatial information, thereby avoiding the SAR image. The addition of spatial structure information has the purpose of reducing spectral distortion[8]; Hong et al.[9] combined the characteristics of panchromatic and SAR images to establish feature selection criteria to adjust the luminance component of multi-spectral images to avoid over-addition. happened. Although such a method based on brightness modulation can effectively overcome the over-addition phenomenon of the traditional HIS method, it is unavoidable to lose some structural information in the SAR image, and it is necessary to make up for the details of the full-color image. Spectral adaptive HIS method is applied to multi-spectral and panchromatic image fusion. This method has good spectral preservation for IKONOS images, but it is not suitable for other remote sensing images[10]; an adaptive HIS method is adaptive. Linear combination coefficients are used to obtain spatial structure information of high-resolution images and added to low-resolution images[11, 12]; HIS methods based on wavelet and empirical decomposition models use wavelet to extract features of SAR images, and empirically decompose model injection features[13]. This kind of method can better inject the spatial structure and feature information of SAR image into visible light image. However, due to the large difference of imaging band range between the two images, the added spatial structure and feature information cannot be smoothed with the surrounding information of the original visible light image. Transition, causing spectral distortion.
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Implementation and Optimization of 4×4 Luminance Intra Prediction Modes on FPGA

Implementation and Optimization of 4×4 Luminance Intra Prediction Modes on FPGA

Intra prediction[1, 2, 3] algorithm is a process to eliminate correlation between neighbouring pixels in a single frame of a video sequence. Neighbouring pixels of reconstructed block in current frame is used for prediction of current block. Luminance component[2] of video is more important than chrominance part because luminance is more sensitive to human visual system (HVS-eye). In 4×4 block size luminance intra prediction a frame is divided into macroblock of 16×16 size. The 16×16 macroblock is subdivided into 4×4 block size and processed from top left block. There are nine modes [2, 3] of luminance intra prediction for 4×4 block size:
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Video Demystified A Handbook for the Digital Engineer 4th ed   keith Jack pdf

Video Demystified A Handbook for the Digital Engineer 4th ed keith Jack pdf

The red, green, and blue (RGB) color space is widely used throughout computer graphics. Red, green, and blue are three pri- mary additive colors (individual components are added together to form a desired color) and are represented by a three-dimensional, Cartesian coordinate system (Figure 3.1). The indicated diagonal of the cube, with equal amounts of each primary component, repre- sents various gray levels. Table 3.1 contains the RGB values for 100% amplitude, 100% satu- rated color bars, a common video test signal. A color space is a mathematical represen-

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The perceived depth from disparity as function of luminance contrast

The perceived depth from disparity as function of luminance contrast

There was, however, a significant interaction between luminance contrast and disparity, such that both the maximum perceived depth difference and the disparity modulation level where this maximum occurred changed as a sigmoid function of luminance (Figure 3). Such sigmoid behavior implies that a contrast gain control mechanism is involved in this contrast effect, providing strong contrast dependence below about 10% contrast (20 dB) and approximate contrast constancy of perceived depth above it. Moreover, the contrast gain control parameters exhibit a strong dependence on the disparity modu- lation spatial frequency (Figure 3), implying the presence of long-range spatial integration across local disparity processing elements. This pattern of behav- ior implies that the results cannot be explained by a disparity energy model, which would predict no luminance contrast effect on perceived depth differ- ence (Ohzawa, DeAngelis, & Freeman, 1990; Fleet, Wagner & Heeger, 1996; Qian, 1997; Qian & Zhu, 1997; Ohzawa, 1998; Read, Parker, & Cumming, 2002).
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Maturation of polarization and luminance contrast sensitivities in cuttlefish (Sepia officinalis)

Maturation of polarization and luminance contrast sensitivities in cuttlefish (Sepia officinalis)

Newly hatched cuttlefish do not benefit from parental care so they need to cope on their own to hunt such silvery and transparent prey (Boletzky et al., 1977). Therefore, it is of interest to examine the ontogenetic development of polarization-based visual capacities and to compare them with luminance contrast-based visual capacities. OMR induced in juvenile Sepia officinalis improved in luminance-contrast-based visual acuity from a minimum separated angle of 2.5deg in cuttlefish measuring 1cm to 0.5deg for cuttlefish measuring 8cm (Groeger et al., 2005). OMR was also used to examine PS in adult cuttlefish of different species, but has not yet been studied in hatchlings (Darmaillacq and Shashar, 2008; Talbot and Marshall, 2010a; Talbot and Marshall, 2010b). Newly hatched cuttlefish are able to visually discriminate between different crab phenotypes, suggesting good detection of prey based on luminance contrast (Guibé et al., 2012).
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Discomfort glare evaluation: the influence of anchor bias in luminance adjustments

Discomfort glare evaluation: the influence of anchor bias in luminance adjustments

The IES-GI was calculated from the rec- orded luminances. After making the initial four evaluations, participants were given a short relaxation period (two minutes) before continuing the experiment starting with the next luminance anchor. The test procedure was again repeated until the subject had provided all four levels of glare sensation under each of the three different luminance anchors. To help mask any unwanted pro- cedural effects (e.g. learning, fatigue), the luminance anchors were presented to each test participant in a randomised sequence. 49 Each test session lasted approximately 30 minutes. Twenty-two subjects volunteered to par- ticipate in this experiment. They were recruited via an online advertisement addressed to all postgraduate students in the Department of Architecture at the University of Nottingham. The sample comprised 8 males and 14 females, with a mean age of 29.6 years (SD ¼ 3.75 years). Seventeen sub- jects wore their normal glasses or corrective lenses, and all self-certified as having no other health or eye problems.
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Area summation of first- and second-order modulations of luminance

Area summation of first- and second-order modulations of luminance

We now compare predictions from the zero-free- parameter noisy energy model (Meese, 2010; Meese & Summers, 2012)—see black dashed curve on the right- most panel of Figure 3—with the area summation results for our LM stimuli. Full details of this model are given in Meese and Summers (2012); we provide only an overview here. First, the input image was filtered (by a 1.25 c/8 horizontal sine-phase filter: spatial-frequency bandwidth of 1.6 octaves, orientation bandwidth of 6258; Meese, 2010) and multiplied by a retinal attenuation surface to simulate spatial inhomogeneity (using the average parameters from Table 5 in Baldwin et al., 2012). Each pixel was then squared and summed over area. The prediction is fair, but slightly underes- timates the overall effects of summation. For compar- ison, the crosses in Figure 3C are the mean and standard error for sensitivity to a 1.25 c/8 luminance grating (with no noise carrier). The model predicts these results very well, suggesting that the minor deviations of the model from the LM results owes to a subtle effect of noise masking, beyond the scope of our modeling here.
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The skylight gradient of luminance helps sandhoppers in sun and moon identification

The skylight gradient of luminance helps sandhoppers in sun and moon identification

The laboratory treatments were carried out using a device described previously (Ugolini et al., 1998; Ugolini et al., 2005). The bowl containing the animals was covered by an opaline white Plexiglas dome (diameter 80cm). The glazed internal surface of the dome (artificial sky) was illuminated by a fibre-optic illuminator (Schott KL1500). The end of the fibre bundle (diameter 8mm) was housed in a tube at the centre of the bowl so that the light source was as close as possible to the centre of the dome. To render the illumination even more uniform, the end of the fibre was equipped with a negative lens (diameter 21mm, focal length 11.7mm). To test the role of the skylight gradient of luminance in discriminating between the sun and the moon, we reproduced an artificial gradient inside the dome similar to the gradient we found under the dome in natural conditions of sun and sky: the dome under natural conditions and the dome under artificial illumination had almost the same luminance ratio (although they differed in absolute luminance levels). To recreate the correct gradient of luminance inside the dome, we placed a hemicycle of grey gelatine filter (Medium Grey no. 210, Spotlight, Milano, Italy) on the fibre simulating the sky in a defocused position so that transition between the two hemicycles was gradual (Fig.1, profile c). Of course, the grey
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Editable View Optimized Tone Mapping For Viewing High Dynamic Range Panoramas On Head Mounted Display

Editable View Optimized Tone Mapping For Viewing High Dynamic Range Panoramas On Head Mounted Display

between each TMO in the same region as well as compare TMOs across the regions. It demonstrates the effectiveness of tone mapping editing as there is an obvious trend of user endorsement on the edited images. The user edited images are even more favorable than the unedited ToneTexture results when viewing regions with extreme luminance values. ToneTexture is still viewer’s first choice in mid range regions as well as windows containing both dark areas and bright areas. Based on Table C.2, analysis of variance shows a p-value is 0.17 for ToneTexture and 0.15 for user edited images. These results indicate that region type has no statistically significant influence on user’s selection. Averaging 40% for human edited images and 45% user endorsement for ToneTexture in all four regions, the observation from user evaluation not only proves ToneTexture produces better image quality, but also demonstrates the effectiveness of tone mapping editing since the percentage of preference for global TMOs goes down from 24% to 15%.
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Luminance cues constrain chromatic blur discrimination in
natural scene stimuli

Luminance cues constrain chromatic blur discrimination in natural scene stimuli

consideration (Wuerger, Morgan, Westland, & Owens, 2000; Wuerger, Owens, & Westland, 2001). The reason may be due to reduced spatial sampling of chromatic information leading to a lower precision in chromatic processing (Peirce, Solomon, Forte, & Lennie, 2008). This reduced sampling may, in turn, be a consequence of chromatic aberration; the visual hardware may reflect the lack of spatial precision in the chromatic signals themselves (De Valois & De Valois, 1988). As a result, luminance may be used for tasks requiring high spatial precision. Conversely, color may be used predominately to process surface properties and to facilitate segmentation and grouping, with only a secondary role in edge detection and localization (Mollon, 1989). If color is mainly used to process surface properties this could explain why it appears to be discounted as a cue to edge perception when luminance information is present.
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Lighting and display screens: Models for predicting luminance limits and disturbance

Lighting and display screens: Models for predicting luminance limits and disturbance

recommendations for luminaires are too strict whilst for others, they are too lax. Partly, this is because of the rapid development in screen technology and partly because of the need for recommendations to be simple to understand. To rectify this situation, the results have been used to construct two empirical models. One model expresses the relationship between four variables related to the lighting and the screen characteristics (size of light source and specular reflectance, effect of haze reflection and background luminance of the screen) and the luminance of the light source that would be acceptable to 95% of observers when reflected in the screen. The other expresses the relationship between five variables (luminance and size of light source and specular reflectance, effect of haze reflection and background luminance of the screen) and the level of disturbance expressed by observers on a six point rating scale. Both models can be used to assign luminaire luminance limits to ensure disturbing reflections are not seen in specific screens or to identify which screens can be used with specific lighting without disturbing reflections. Given information about a range screen types currently in use, the models could also be used as the basis for generating more general
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Effects of chronic alcoholism in the sensitivity to luminance contrast in vertical sinusoidal gratings

Effects of chronic alcoholism in the sensitivity to luminance contrast in vertical sinusoidal gratings

In this context, it is necessary to conduct further research seeking to refine the sample characteristics to establish intra and more homogeneous parameters between groups and thus further elucidate the effect of chronic alcohol use on visual perception of achromatic luminance contrast. Although the effects of moderate or acute alcohol consumption on visual CS are described by several studies reporting alterations in medium and high frequencies with greater damage in the range of spatial frequencies of 3.0 and 6.0 cpd (Andre, 1996; Nicholson, Andre, Tyrrell, Wang, & Leibowitz 1995; Weschke & Niedeggen 2012; Zhuang et al., 2012). Researches with human volunteers with histories of chronic consumption and in abstinent state are still little explored.
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Effect of Screen Contrast Ratio and Luminance Level on Visual Lobe Shape

Effect of Screen Contrast Ratio and Luminance Level on Visual Lobe Shape

results also accounted for the better visual performance resulted with increasing screen luminance combination. Sanders and McCormick suggested that the most suitable luminance ratio between immediate surroundings and the screen was 1:3 [32]. In the current study, high levels of lobe roundness, boundary smoothness, symmetry, and regularity were noted for lobe mapped under 1:3 contrast ratio condition. The results were concurrent with Sanders and McCormick’s recommendation as a good visual lobe shape was noted for lobe of 1:3 contrast ratio. Lobe shape characteristics of 1:4 contrast ratio was generally similar to that of 1:3 contrast ratio. The results account for the non-significant improvement on visual recognition from contrast ratio increased from 1:3 to 1:4 found by Chen and Lin [34].
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Interocular interaction of contrast and luminance signals in human primary visual cortex

Interocular interaction of contrast and luminance signals in human primary visual cortex

The icons in the panel indicates five different conditions as follows: Solid blue: monocular response at 4Hz; Dashed blue: power loss due to a 6Hz dichoptic mask; Dashed red: response to[r]

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Road lighting research for drivers and pedestrians: The basis of luminance and illuminance recommendations

Road lighting research for drivers and pedestrians: The basis of luminance and illuminance recommendations

STV is stated to be the basis of some road lighting standards for drivers 31 although standards themselves tend to be somewhat vague about the basis of recommendations. While it was hinted in CIE115:1995 32 that visibility was the basis of the luminance recommendations, it was also stated that ‘These recommendations are based on research on and experience in all aspects of the visual requirements at night’ and furthermore that ‘Although prescribed values of the criteria were originally arrived at as a result of experimental work, they have been tempered by experience over this time . . . ’ Table 4 shows light levels recommended in CIE115:1995 for the M-class roads, where Table 6.1 of that document gives recommendations without clear acknowledge- ment of the basis and Table A1 of that document gives recommendations based on STV. This suggests that the main recommen- dations were not based on STV. Note that M- series of lighting classes as shown in Table 4 are ‘intended for drivers of motorized vehicles on traffic routes, and in some countries also on residential roads, allowing medium to high driving speeds’; 1 for pedestrian and low
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