New Method For Image Compression Research Articles

Image compression in all-optical domain using one 6×6 multimode interference coupler

We present a new method for image compression using the Haar wavelet transform (HWT) in all-optical domain. The proposed architecture is based on the optical Haar wavelet transform using only one 6×6 multimode interference (MMI) coupler. By locating the positions of input and output waveguides and optimizing design length of the MMI, the expected matrix of the Haar transform is obtained. The new hardware architecture is suitable for directly integrating with digital cameras for image processing. The processing of images therefore is at very high speed. Our method can also be applied to data compression in big data analytics. Our structure can provide a large fabrication tolerance which is compatible with the CMOS existing technology. Our simulations show that the length variation of ± 2 µm still keeps the output powers unchanged. We have simulate successfully the use of the proposed HWT to compress cameraman image with the compressed ratios of 20, 30, 50 % with MSE and PSNR from 0.1 - 0.3 and 62 – 67 dB, respectively.

On-chip All-optical Haar Transform based on a 4x4 MMI coupler cascaded with a 2x2 MMI coupler for Image Compression

We present a new method for image compression in all-optical domain. The new hardware architecture is suitable for directly integrating with digital cameras for image processing. The proposed architecture is based on the optical Haar wavelet transform (HWT) using only one 4x4 multimode interference (MMI) coupler cascaded with a 2x2 MMI coupler. The processing of images therefore is at very high speed.

A novel high-frequency encoding algorithm for image compression

In this paper, a new method for image compression is proposed whose quality is demonstrated through accurate 3D reconstruction from 2D images. The method is based on the discrete cosine transform (DCT) together with a high-frequency minimization encoding algorithm at compression stage and a new concurrent binary search algorithm at decompression stage. The proposed compression method consists of five main steps: (1) divide the image into blocks and apply DCT to each block; (2) apply a high-frequency minimization method to the AC-coefficients reducing each block by 2/3 resulting in a minimized array; (3) build a look up table of probability data to enable the recovery of the original high frequencies at decompression stage; (4) apply a delta or differential operator to the list of DC-components; and (5) apply arithmetic encoding to the outputs of steps (2) and (4). At decompression stage, the look up table and the concurrent binary search algorithm are used to reconstruct all high-frequency AC-coefficients while the DC-components are decoded by reversing the arithmetic coding. Finally, the inverse DCT recovers the original image. We tested the technique by compressing and decompressing 2D images including images with structured light patterns for 3D reconstruction. The technique is compared with JPEG and JPEG2000 through 2D and 3D RMSE. Results demonstrate that the proposed compression method is perceptually superior to JPEG with equivalent quality to JPEG2000. Concerning 3D surface reconstruction from images, it is demonstrated that the proposed method is superior to both JPEG and JPEG2000.

SELF ORGANIZING MAP BASED IMAGE COMPRESSION USING CDF 9/7 LIFT DISCRETE WAVELET TRANSFORM

In this paper a new method for image compression using Self Organizing Map and CDF 9/7 lift Discrete Wavelet Transform is proposed. Here Self Organizing Map is used to generate the code vectors associated with the input data there by reducing the dimension of the input data. Discrete Cosine Transform is used in the existing methods of image compression which results in the degradation of the reconstructed image. Hence to improve the reconstructed image quality 9/7 lift DWT is used. Peak Signal to Noise Ratio (PSNR) and Compression ratio (CR) is calculated for various images using the proposed method and they show better results than the existing methods.

The Image Compression Technology Based on Wavelet Transform

Image compression is a data compression technology used in the digital image, its purpose is to reduce redundant information of the image data, and provide a more efficient format to store and transmit data. Due to the huge image data and the existing relatively low transport conditions, the image compression has become an inevitable. The key technology of image compression is how to transform image data, how to quantify image data, and how to entropy code the quantized data. Using two-dimensional Mallat image wavelet compression algorithm is a new method of image compression, and it is the core technology of the wavelet image compression.

Morphological Transform for Image Compression

A new method for image compression based on morphological associative memories (MAMs) is presented. We used the MAM to implement a new image transform and applied it at the transformation stage of image coding, thereby replacing such traditional methods as the discrete cosine transform or the discrete wavelet transform. Autoassociative and heteroassociative MAMs can be considered as a subclass of morphological neural networks. The morphological transform (MT) presented in this paper generates heteroassociative MAMs derived from image subblocks. The MT is applied to individual blocks of the image using some transformation matrix as an input pattern. Depending on this matrix, the image takes a morphological representation, which is used to perform the data compression at the next stages. With respect to traditional methods, the main advantage offered by the MT is the processing speed, whereas the compression rate and the signal-to-noise ratio are competitive to conventional transforms.

Lossy dictionary-based image compression method

In this paper, we report on the new method of image compression. The method is based on LZ77 dictionary algorithm. We introduce two modifications such as quantization and noise levels. Experimental results presented in this paper prove that the new method of image compression gives promising results as compared with original LZ77 dictionary algorithm and JPEG2000.

Image compression by linear splines over adaptive triangulations

This paper proposes a new method for image compression. The method is based on the approximation of an image, regarded as a function, by a linear spline over an adapted triangulation, D ( Y ) , which is the Delaunay triangulation of a small set Y of significant pixels. The linear spline minimizes the distance to the image, measured by the mean square error, among all linear splines over D ( Y ) . The significant pixels in Y are selected by an adaptive thinning algorithm, which recursively removes less significant pixels in a greedy way, using a sophisticated criterion for measuring the significance of a pixel. The proposed compression method combines the approximation scheme with a customized scattered data coding scheme. We compare our compression method with JPEG2000 on two geometric images and on three popular test cases of real images.

K-0301 画像圧縮へのマルチウエーブレットの応用(S01-1 ウェーヴレットの最新応用(1))(S01 ウェーヴレットの最新応用)

This paper describes a new method for image compression which is based on multiwavelets. In 1994, Geronimo, Hardin and Massopust constructed a very important multiwavelet system. This system consists of two scaling functions and two wavelets. The scaling functions are both symmetric, and one of the wavelets is symmetric and the other is antisymmetric. Moreover, they each have compact support. In this talk we present a new preprocess of image data which decrease mean-square error of image compression based on multiwavelets.

Counter‐propagation neural network for image compression

Recently, several image compression techniques based on neural network algorithms have been developed. In this paper, we propose a new method for image compression—the modified counterpropagation neural network algorithm, which is a combination of the selforganizing map of Kohonen and the outstar structure of Grossberg. This algorithm has been successfully used in many applications. The modification presented has also demonstrated an interesting performance in comparison with the standard techniques. It was found that at the learning stage we can use any image for a network training (without a significant influence on the net operation) and the compression ratio and quality depend on the size of the basic element (the number of pixels in the cluster) and the amount of error tolerated when processing.

Image compression by edge pattern learning using multilayer perceptrons

A new method for image compression using several multilayer perceptron (MLP) networks is described. Images to be compressed are divided into small sub-image blocks which are processed independently. The MLPs are used to code residual image blocks, formed by subtracting the quantised block mean value from each pixel in the original block. To reduce edge degradation in the reconstructed images, several MLPs are used, each representing a different orientation of edge information. Simulation results are presented which demonstrate the effectiveness of this new technique.

A new method of image compression using irreducible covers of maximal rectangles

The binary-image-compression problem is analyzed using irreducible cover of maximal rectangles. A bound on the minimum-rectangular-cover problem for image compression is given under certain conditions that previously have not been analyzed. It is demonstrated that for a simply connected image, the irreducible cover proposed uses less than four times the number of the rectangles in a minimum cover. With n pixels in a square, the parallel algorithm for obtaining the irreducible cover uses (n/log n) concurrent-read-exclusive write (CREW) processors in O(log n) time.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>