Palette-based Images Research Articles

Building Coarse to Fine Convex Hulls With Auxiliary Vertices for Palette-Based Image Recoloring.

Constructing a convex hull for the pixel colors of an image by viewing them as 3D points can extract a set of palette colors for the image, then image recoloring can be achieved by modifying the palette colors. For better recoloring effect, the convex hull should contain more pixels (inclusive) and be more compact. Otherwise, reconstruction error would occur or the extracted palette color would be less representative, yielding wrong recoloring results or less effective edit. We observe that convex hulls constructed by prior methods can contain all the image pixels, but are far from compact. Efforts have been made to optimize the vertices of convex hull to increase the compactness but are still not perfect. In this paper, we propose a novel coarse to fine convex hull construction scheme with auxiliary vertices. We start by constructing a coarse convex hull whose vertices are directly image pixels which is thus the most compact but cannot contain all pixels. We then make a remedy by adding auxiliary vertices into the coarse convex hull to obtain a fine convex hull. More auxiliary vertices are added, more image pixels will be contained into the fine convex hull. The auxiliary vertices are image pixels too so that the compactness can still be maintained. During editing, the auxiliary vertices are not allowed to be edited for edit convenience, but deformed as-rigid-as-possible with the adjusting of other vertices. Our convex hull is both inclusive and compact. Extensive experiments validate the effectiveness of the proposed method.

Prospects of Palette-based Image Retrieval and Palette-based Design Support

Prospects of Palette-based Image Retrieval and Palette-based Design Support

A Blind Color Separation Model for Faithful Palette-Based Image Recoloring

Palette-based image recoloring provides a simple yet effective way for color adjustment, which allows users to interactively manipulate the color of an image by editing a compact color palette. While remarkable progress has been made by previous methods, they have the common limitations that may produce unfaithful image recoloring results i.e., the obtained result does not respond faithfully to the palette adjustment, and tend to induce visual artifacts such as color bleeding and distortion. To address these limitations, we in this paper present a novel color separation model for palette-based recoloring. Akin to previous methods, our color separation model is built upon the assumption that color of each pixel in an image can be formulated as a linear combination of a small set of same basis colors. However, different from previous palette-based recoloring methods which typically rely on heuristic rules to build the color separation model, we experimentally reveal the underlying relationship between the color separation and the palette-based recoloring, and summarize three specialized color separation priors that allow more faithful palette-based recoloring. Based on these priors, we devise a blind color separation model that not only does not require known palette as input as done in previous methods, but also enables more effective palette-based recoloring with much less visual artifacts. Experiments on two datasets demonstrate that our method outperforms the state-of-the-art palette-based recoloring methods. In addition, we show some applications enabled by the proposed color separation model, including automatic pattern coloring generation, green screen keying and region-controllable color transfer.

Undetectable steganographic method for palette-based images

Steganography is a technique applied to ensure secure communication. It is challenged by visual observation or statistical analysis to ascertain whether a message is hidden within a cover medium. Several steganographic methods have been proposed for palette-based images. These methods maintain image quality but cannot resist some statistical and visual attacks. To overcome this problem, two parity assignments with performances similar to those of existing parity assignments are proposed. An innovative embedding process that randomly selects one parity assignment while embedding secret bits in each pixel is provided. Finally, a steganographic method using the proposed embedding process and a specified adaptive scheme is presented. Experimental results revealed that the proposed method is undetectable under some statistical and visual attacks even as it maintains image quality.

Efficient palette-based decomposition and recoloring of images via RGBXY-space geometry

We introduce an extremely scalable and efficient yet simple palette-based image decomposition algorithm. Given an RGB image and set of palette colors, our algorithm decomposes the image into a set of additive mixing layers, each of which corresponds to a palette color applied with varying weight. Our approach is based on the geometry of images in RGBXY-space. This new geometric approach is orders of magnitude more efficient than previous work and requires no numerical optimization. We provide an implementation of the algorithm in 48 lines of Python code. We demonstrate a real-time layer decomposition tool in which users can interactively edit the palette to adjust the layers. After preprocessing, our algorithm can decompose 6 MP images into layers in 20 milliseconds.

Selectively Embeddable Multi-bit Steganographic Method for Palette-based Images

Selectively Embeddable Multi-bit Steganographic Method for Palette-based Images

Color quantization method based on principal component analysis and linear discriminant analysis for palette-based image generation

High performance of color quantization processing is very important for obtaining limited-color images with good quality. The median cut algorithm (MCA) is a typical color quantization method. Its computational cost is low owing to its simple algorithm, but the quality of output images is mediocre at best. In this paper, we describe a modification of MCA. In our method, we use a combination of principal component analysis (PCA) and linear discriminant analysis (LDA) to accomplish effective partitioning of color space. Concretely, PCA and LDA are used to calculate partitioning planes and their positions, respectively. We verify the effectiveness of our method through experiments using 24-bit full-color natural images.

Palette-Based Image Steganography for High-Capacity Embedding

Palette-Based Image Steganography for High-Capacity Embedding

A reversible data-hiding method for palette-based images with capacity optimization

This study presents a reversible data-hiding scheme based on palette image histogram characteristics, in which the least frequent color entries and the most frequent color entries are identified to imbed data. Data are concealed by sacrificing many sets of the least frequent colors, and placing one of the most frequent colors in their positions in the palette to make a ‘cluster’ for each set. Different bits of data can be imbedded using different sized clusters. A capacity optimization scheme is then developed to obtain the maximum capacity by estimating all possible combinations of clusters. For image reconstruction, making the host image reversible requires saving the overhead information caused by sacrificing the least frequent colors. Effectiveness of the proposed method is also demonstrated on test images by showing the capacity and distortion. Importantly, the proposed method has a high imbedding capacity and excellent marked image quality.

Steganalysis for palette-based images using generalized difference image and color correlogram

In this paper, we attempt to propose a novel blind steganalysis algorithm for palette-based images. First, the generalized difference images between adjacent pixels were constructed, and then the moments of characteristic functions of difference images' histograms were extracted as features. Second, in order to measure the dependencies of neighboring colors, color correlogram technique is used to capture the global distribution of local spatial correlation of colors. The center of mass of the characteristic function of color correlogram and the absolute moments of autocorrelogram were extracted. Total of 13 dimension features were classified with machine learning technique. Number of experiments on several existing GIF steganography algorithms indicated that the proposed scheme is effective and gets good performance, especially when the embedding rate is not less than 20%. Experimental results also show that the average accuracy of our proposed scheme for different GIF steganography algorithms outperforms Lyu's algorithm more than 20%. It also showed that the proposed scheme achieved similar performance with Fridrich's scheme and higher accuracies comparing to Du's algorithm and biologically inspired features.

Reversible data-hiding method for palette-based images

This study proposes a novel reversible data-hiding method based on an iterative approach for palette-based images. In each iteration, the number of occurrences of palette color in the image is computed in order to identify the most and least frequently occurring colors in a palette for data hiding. Data are hidden by sacrificing the least frequent color and placing the most frequent color in its position in the palette to make a pair. After the most frequent color takes over the palette index of the least frequent color, two palette indices stand for the most frequent color. Therefore, one bit can be hidden in every pixel where the most frequent color occurs in the image. To make the host image reversible, the overhead information caused by sacrificing the least frequent color must be saved for image reconstruction. The performance of the proposed method is demonstrated on test images by showing the capacity and distortion. Several successful similar efforts have also been implemented for comparison. Experimental results indicate that the proposed method has a high embedding capacity and good marked-image quality.

A Note on Zeng's Technique for Color Reindexing of Palette-Based Images

Palette reindexing is a well-known and very effective approach for improving the compression of color-indexed images. In this letter, we address the reindexing technique proposed by Zeng et al. and we show how its performance can be improved through a theoretically motivated choice of parameters. Experimental results show the practical appropriateness of the proposed modification.

An iterative method of palette-based image steganography

This article presents a novel iterative method of palette-based image steganography that minimizes the RMS error between an original image and its stego-image. The proposed method is based on a palette modification scheme, which can iteratively embed one message bit into each pixel in a palette-based image. In each iteration, both the cost of removing an entry color in a palette and the benefit of generating a new one to replace it are calculated. If the maximal benefit exceeds the minimal cost, a entry color is replaced. Experimental results show that the proposed method remarkably reduces the distortion of the carrier images (stego-images) to other palette-based methods.