Storage Of Color Images Research Articles

Efficient storage and classification of color patterns based on integrating interpolation with ANN/SVM

Color images usually have large storage sizes as they are composed of three planes in the raw image format represented with the red, green, and blue components. Traditional color image compression schemes can be used to save the storage size of the color images. Unfortunately, most of these schemes are lossy in nature, which affects the details of color images. This paper presents a different treatment to the color image storage problem depending on the original color image formation process. In the color image formation process, not all the red, green, and blue components of the color images are acquired, simultaneously. Only, one component at each pixel position is acquired and Color Filter Array (CFA) interpolation is used to estimate the other two components using interpolation algorithms like Minimized-Laplacian Residual Interpolation (MLRI) and Linear Interpolation with Laplacian Second Order Correction (LILSOC). We adopt a similar strategy in this paper for reducing the storage sizes of color images by 66.67% of their original sizes. The sensitivity of the pattern recognition process to the proposed color image storage and interpolation strategy is studied in this paper. A cepstral feature extraction algorithm is adopted in this paper for extracting features from the interpolated patterns for further classification. Moreover, two types of classifiers are considered and compared in this paper for the pattern recognition: Artificial Neural Networks (ANNs), and Support Vector Machines (SVMs). Simulation results reveal the success of the proposed strategy for color image storage and interpolation in obtaining high-quality color images in addition to the high Recognition Rates (RR) of color patterns after interpolation. This success encourages the use of the proposed color image storage strategy in storing large volumes of color databases.

Color image storage and retrieval using quantum mechanics

In this paper, two algorithms for storing and retrieving a color image using quantum mechanics are proposed. The first algorithm encodes a pixel’s color information with an angle. In this case, it has some advantages in retrieval speed, required qubits and quantum measurements. In the second algorithm, a pixel’s color is divided into three channels: R, G and B, and each channel is encoded with an angle. In this case, it has an advantage in retrieval accuracy. By example analysis, the proposed algorithms are compared with the other algorithms in some aspects, e.g., the total number of qubits, the total number of measurements and the accuracy of retrieval. It can be found that the first algorithm has the advantage in the speed of retrieval, the total number of qubits and the total number of measurements, and the second algorithm has the advantage in the accuracy of retrieval.

Multidimensional color image storage, retrieval, and compression based on quantum amplitudes and phases

In this study, we propose a new representation method for multidimensional color images, called an n-qubit normal arbitrary superposition state (NASS), where n qubits represent the colors and coordinates of 2n pixels (e.g., a three-dimensional color image of 1024×1024×1024 using only 30 qubits). Based on NASS, we present an (n+1)-qubit normal arbitrary superposition state with relative phases (NASSRP) and an (n+2)-qubit normal arbitrary superposition state with three components (NASSTC) for lossless and lossy quantum compression, respectively. We also design three general quantum circuits to generate NASS, NASSRP, and NASSTC states, where we retrieve an image from a quantum system using different projection measurement operators. Finally, we define the quantum compression ratio and analyze lossless and lossy quantum compression algorithms of multidimensional quantum images. For the first time, we implemented the compression of multidimensional color images on a quantum computer. Thus, we address the theoretical and practical aspects of image processing on a quantum computer.

Multi-dimensional color image storage and retrieval for a normal arbitrary quantum superposition state

Multi-dimensional color image processing has two difficulties: One is that a large number of bits are needed to store multi-dimensional color images, such as, a three-dimensional color image of $$1024 \times 1024 \times 1024$$1024?1024?1024 needs $$1024 \times 1024 \times 1024 \times 24$$1024?1024?1024?24?bits. The other one is that the efficiency or accuracy of image segmentation is not high enough for some images to be used in content-based image search. In order to solve the above problems, this paper proposes a new representation for multi-dimensional color image, called a $$(n\,+\,1)$$(n+1)-qubit normal arbitrary quantum superposition state (NAQSS), where $$n$$n qubits represent colors and coordinates of $${2^n}$$2n pixels (e.g., represent a three-dimensional color image of $$1024 \times 1024 \times 1024$$1024?1024?1024 only using 30?qubits), and the remaining 1?qubit represents an image segmentation information to improve the accuracy of image segmentation. And then we design a general quantum circuit to create the NAQSS state in order to store a multi-dimensional color image in a quantum system and propose a quantum circuit simplification algorithm to reduce the number of the quantum gates of the general quantum circuit. Finally, different strategies to retrieve a whole image or the target sub-image of an image from a quantum system are studied, including Monte Carlo sampling and improved Grover's algorithm which can search out a coordinate of a target sub-image only running in $$O(\sqrt{N/r} )$$O(N/r) where $$N$$N and $$r$$r are the numbers of pixels of an image and a target sub-image, respectively.

Quantale-based autoassociative memories with an application to the storage of color images

In recent years, lattice computing has emerged as a new paradigm for processing lattice ordered data such as intervals, Type-1 and Type-2 fuzzy sets, vectors, images, symbols, graphs, etc. Here, the word “lattice” refers to a mathematical structure that is defined as a special type of a partially ordered set (poset). In particular, a complete lattice is a poset that contains the infimum as well as the supremum of each of its subsets. In this paper, we introduce the quantale-based associative memory (QAM), where the notion of a quantale is defined as a complete lattice together with a binary operation that commutes with the supremum operator. We show that QAMs can be effectively used for the storage and the recall of color images.

Archival characteristics of Fourier color holograms

The archival characteristics of Fourier color holograms (FCH) have been analyzed. The color registrations are not affected by film binding, shrinkage, and wavefront deviation of the reconstruction beam. The image contrast has been analyzed using a simple Fourier decomposition model. This model predicts that the image contrast is preserved if films of low negative gamma (-2 </= gamma < 0) are used. The exact value of gamma is unimportant. The image distortions have been determined. The optimum wavelengths for FCH recording are suggested. It appears that the FCH is nearly ideal for archival color image storage applications.