In digital cinema, an image goes through many types of processes like scanning, mastering, and digital intermediate. Among them, the digital intermediate process plays a central role because it determines the final color of an image. It edits and changes the colors of the images. However, some color distortions such as color bleeding are generated when editing and changing local colors in an image. In this paper, local color improvement for digital intermediate is proposed based on color transfer. Our method is simple and efficient color improvement that does not requires neither precise image segmentation nor feature matching. To prevent color distortions, a modified color influence map is proposed with color categories. First, the source image is roughly segmented using a color category map, which groups similar colors in color space. Second, the color influence map is modified by assigning different weights to the lightness and chroma components. Lastly, the modified color influence map and color category map filtered with anisotropic diffusion are combined. Experimental results show that the proposed method produces less color distortion in the resulting image.

Person re-identification is a challenging problem of matching observations of individuals across non-overlapping camera views. When pedestrians walk across disjoint camera views, continuous motion information is lost, and thus re-identification mainly relies on appearance matching. Person re-identification is actually a special case of near duplicate search in image retrieval. Given a probe, our task is to find the image containing the same person in galleries. At present many state-of-the-art methods in image retrieval are based on the Bag-of-Words (BOW) model. By adapting the BOW model to our task, Bag-of-Ensemble-Colors (BOEC) is proposed to tackle person re-identification in this paper. We combine low-level color histogram and semantic color names to represent human appearances. Meanwhile, some mature and efficient techniques in image retrieval are employed in the model containing soft quantization, burstiness punishing strategy, and negative evidence. In consideration apriori knowledge of human body structure, efficient spatial constraints are proposed to weaken the influence of background. Extensive experiments on VIPeR and ETHZ databases are performed to test the effectiveness of our approach, and promising results are obtained in the public databases. Compared with other unsupervised methods, we obtain state-of-the-art performances. The recognition rate is 32.23% on VIPeR dataset, 87% on ETHZ SEQ.#1, 83% on ETHZ SEQ.#2, and 91% on ETHZ SEQ.#3.

Multimedia contents can be enriched by introducing navigation with image codes readable by camera-mounted mobile devices such as smartphones. Data hiding technologies were utilized for embedding such codes to make their appearances inconspicuous, which can reduce esthetic damage on visual media. This article proposes a method of embedding two-dimensional codes into images based on successive color mixture for a blue-color channel. This technology can make the color of codes mimic those used on a cover image, while preserving their readability for current general purpose image sensors.

We propose an unsharp-masking technique which preserves the hue of colors in images. This method magnifies the contrast of colors and spatially sharpens textures in images. The contrast magnification ratio is adaptively controlled. We show by experiments that this method enhances the color tone of photographs while keeping their perceptual scene depth.

An orthogonal ray graph is an intersection graph of horizontal and vertical rays (closed half-lines) in the plane. Such a graph is 3-directional if every vertical ray has the same direction, and 2-directional if every vertical ray has the same direction and every horizontal ray has the same direction. We derive some structural properties of orthogonal ray graphs, and based on these properties, we introduce polynomial-time algorithms that solve the dominating set problem, the induced matching problem, and the strong edge coloring problem for these graphs. We show that for 2-directional orthogonal ray graphs, the dominating set problem can be solved in O(n2 log5 n) time, the weighted dominating set problem can be solved in O(n4 log n) time, and the number of dominating sets of a fixed size can be computed in O(n6 log n) time, where n is the number of vertices in the graph. We also show that for 2-directional orthogonal ray graphs, the weighted induced matching problem and the strong edge coloring problem can be solved in O(n2+m log n) time, where m is the number of edges in the graph. Moreover, we show that for 3-directional orthogonal ray graphs, the induced matching problem can be solved in O(m2) time, the weighted induced matching problem can be solved in O(m4) time, and the strong edge coloring problem can be solved in O(m3) time. We finally show that the weighted induced matching problem can be solved in O(m6) time for orthogonal ray graphs.

In the impulse noise removal from a color image, vector filters are suitable for suppressing false color generation. However, the vector filters do not select optimal vectors to restore noise corrupted pixels. To cope with this problem, a cost function-based vector filter is proposed in this letter.

In dense femtocell networks (DFNs), one of the main issues is interference management since interference between femtocell access points (FAPs) reduces the system performance significantly. Further, FAPs serve different numbers of femtocell user equipments (FUEs), i.e., some FAPs have more than one FUE while others have one or no FUEs. Therefore, for DFNs, an intelligent channel assignment scheme is necessary considering both the number of FUEs connected to the same FAPs and interference mitigation to improve system performance. This paper proposes a two-stage dynamic channel assignment (TS-DCA) scheme for downlink DFNs based on orthogonal frequency division multiple access/frequency division duplex (OFDMA/FDD). In stage 1, using graph coloring algorithm, a femtocell gateway (FGW) first groups FUEs based on an interference graph that considers different numbers of FUEs per FAP. Then, in stage 2, the FGW dynamically assigns subchannels to FUE clusters according to the order of maximum capacity of FAP clusters. In addition, FAPs adaptively assign remaining subchannels in FUE clusters to their FUEs in other FUE clusters. Through simulations, we first find optimum parameters of the FUE clustering to maximize the system capacity and then evaluate system performance in terms of the mean FUE capacity, unsatisfied FUE probability, and mean FAP transmission energy consumption according to the different numbers of FUEs and FAPs with a given FUE traffic load.

In this paper, we propose a novel method for road sign detection and recognition in complex scene real world images. Our algorithm consists of four basic steps. First, we employ a regional contrast based bottom-up visual saliency method to highlight the traffic sign regions, which usually have dominant color contrast against the background. Second, each type of traffic sign has special color distribution, which can be explored by top-down visual saliency to enhance the detection precision and to classify traffic signs into different categories. A bag-of-words (BoW) model and a color name descriptor are employed to compute the special-class distribution. Third, the candidate road sign blobs are extracted from the final saliency map, which are generated by combining the bottom-up and the top-down saliency maps. Last, the color and shape cues are fused in the BoW model to express blobs, and a support vector machine is employed to recognize road signs. Experiments on real world images show a high success rate and a low false hit rate and demonstrate that the proposed framework is applicable to prohibition, warning and obligation signs. Additionally, our method can be applied to achromatic signs without extra processing.

In this paper, we propose a new matting algorithm using local and nonlocal neighbors. We assume that K nearest neighbors satisfy the color line model that RGB distribution of the neighbors is roughly linear and combine this assumption with the local color line model that RGB distribution of local neighbors is roughly linear. Our assumptions are appropriate for various regions such as those that are smooth, contain holes or have complex color. Experimental results show that the proposed method outperforms previous propagation-based matting methods. Further, it is competitive with sampling-based matting methods that require complex sampling or learning methods.

In this study, we address the problem of salient region detection. Recently, saliency detection with contrast based approaches has shown to give promising results. However, different individual features exhibit different performance. In this paper, we show that the combination of color uniqueness and color spatial distribution is an effective way to detect saliency. A Color Adaptive Thresholding Watershed Fusion Segmentation (CAT-WFS) method is first given to retain boundary information and delete unnecessary details. Based on the segmentation, color uniqueness and color spatial distribution are defined separately. The color uniqueness denotes the color rareness of salient object, while the color spatial distribution represents the color attribute of the background. Aiming at highlighting the salient object and downplaying the background, we combine the two characters to generate the final saliency map. Experimental results demonstrate that the proposed algorithm outperforms existing salient object detection methods.

In this paper, a novel White-RGB (WRGB) color filter array-based imaging system for cell phone is presented to reduce noise and reproduce color in low illumination. The core process is based on adaptive diagonal color separation to recover color components from a white signal using diagonal reference blocks and location-based color ratio estimation in the luminance space. The experiments, which are compared with the RGB and state-of-the-art WRGB approaches, show that our imaging system performs well for various spatial frequency images and color restoration in low-light environments.

This letter presents a no-reference blocking artifact measure based on analysis of color discontinuities in YUV color space. Color shift and color disappearance are first analyzed in JPEG images. For color-shifting and color-disappearing areas, the blocking artifact scores are obtained by computing the gradient differences across the block boundaries in U component and Y component, respectively. An overall quality score is then produced as the average of the local ones. Extensive simulations and comparisons demonstrate the efficiency of the proposed method.

Traditional face swapping technologies require that the faces of source images and target images have similar pose and appearance (usually frontal). For overcoming this limit in applications this paper presents a pose-free face swapping method based on personalized 3D face modeling. By using a deformable 3D shape morphable model, a photo-realistic 3D face is reconstructed from a single frontal view image. With the aid of the generated 3D face, a virtual source image of the person with the same pose as the target face can be rendered, which is used as a source image for face swapping. To solve the problem of illumination difference between the target face and the source face, a color transfer merging method is proposed. It outperforms the original color transfer method in dealing with the illumination gap problem. An experiment shows that the proposed face reconstruction method is fast and efficient. In addition, we have conducted experiments of face swapping in a variety of scenarios such as children's story book, role play, and face de-identification stripping facial information used for identification, and promising results have been obtained.

This paper proposes an improved color barycenter model (CBM) and its separation for automatic road sign (RS) detection. The previous version of CBM can find out the colors of RS, but the accuracy is not high enough for separating the magenta and blue regions and the influence of number with the same color are not considered. In this paper, the improved CBM expands the barycenter distribution to cylindrical coordinate system (CCS) and takes the number of colors at each position into account for clustering. Under this distribution, the color information can be represented more clearly for analyzing. Then aim to the characteristic of barycenter distribution in CBM (CBM-BD), a constrained clustering method is presented to cluster the CBM-BD in CCS. Although the proposed clustering method looks like conventional K-means in some part, it can solve some limitations of K-means in our research. The experimental results show that the proposed method is able to detect RS with high robustness.

In this letter, a novel color removal method considering differences of colors in an input color image and achromatic color preservation is proposed. The achromatic color preservation is assigning lightness values to gray-levels concerning achromatic pixels for natural impression. The effectiveness and validity of the proposed method are verified by experiments.

In this paper, we propose a chromatic adaptation model based on the adapting degree according to the level of adapting luminance and chromaticity in various surround illuminants. In the proposed model, first maximum adapted cone responses are calculated through the estimation of adapting degree for viewing conditions then corresponding colors are reproduced from original colors using the ratio of maximum adapted cone responses between different viewing conditions. The purpose of this study is to produce chromatic adaptation transform applied to environment-adaptive color display system. As a result, our proposed model can give better estimation performance than prior models and be embodied easily as a linear model in display systems. So it is confirmed that the implemented system can predict corresponding-color data very well under a variety of viewing conditions.

We designed multilayer wavelength-selective reflector films by stacking thin-films of transparent polymer. The optimum structure of the multilayer is determined using a combination of characteristic matrix method and a version of genetic algorithm. Such multilayer films can be used in LCD devices to enhance the color saturation of the display.

In this paper, we propose a novel technique for film grain noise removal and synthesis that can be adopted in high fidelity video coding. Film grain noise enhances the natural appearance of high fidelity video, therefore, it should be preserved. However, film grain noise is a burden to typical video compression systems because it has relatively large energy levels in the high frequency region. In order to improve the coding performance while preserving film grain noise, we propose film grain noise removal in the pre-processing step and film grain noise synthesis in the post processing step. In the pre-processing step, the film grain noise is removed by using temporal and inter-color correlations. Specifically, color image denoisng using inter color prediction provides good denoising performance in the noise-concentrated B plane, because film grain noise has inter-color correlation in the RGB domain. In the post-processing step, we present a noise model to generate noise that is close to the actual noise in terms of a couple of observed statistical properties, such as the inter-color correlation and power of the film grain noise. The results show that the coding gain of the denoised video is higher than for previous works, while the visual quality of the final reconstructed video is well preserved.

The iCAM06 has been used as an image appearance model for HDR image rendering. iCAM06 goes through the color space conversions of the several steps to present HDR images. The dynamic range of a HDR image needs to be mapped onto the range of output devices, which is called the tone mapping. However, tone compression process of iCAM06 causes white point shift and color distortion because of color-clipping and cross-stimulus. Therefore, we proposed a modified white-balanced method in low-chromatic region and a color adjustment method in IPT space to compensate the color distortion during in tone compression process. Through the experimental results, we confirmed the proposed compatible color adjustment method had better performance than iCAM06 and enhanced models.

Segmenting indicated objects from natural color images remains a challenging problem for researches of image processing. In this paper, a novel level set approach is presented, to address this issue. In this segmentation algorithm, a contour that lies inside a particular region of the concerned object is first initialized by a user. The level set model is then applied, to extract the object of arbitrary shape and size containing this initial region. Constrained on the position of the initial contour, our proposed framework combines two particular energy terms, namely local and global energy, in its energy functional, to control movement of the contour toward object boundaries. These energy terms are mainly based on graph partitioning active contour models and Bhattacharyya flow, respectively. Its flow describes dissimilarities, measuring correlative relationships between the region of interest and surroundings. The experimental results obtained from our image collection show that the suggested method yields accurate and good performance, or better than a number of segmentation algorithms, when applied to various natural images.