Papers by Keyword: Image Compression

Abstract: The Mycielski method is a prospering prediction algorithm which is based on searching and finding largest repeated binary patterns. It uses infinite-past data to devise a rule based prediction method on a time series. In this work, a novel two-dimensional (image processing) version of the Mycielski algorithm is proposed. Since the dimensionality definition of “past” data increases in two-dimensional signals, the proposed algorithm also needs to handle how the boundaries of the pixel cliques are iteratively extended in the neighborhood of a current pixel. The clique extension invokes novel similarity search strategies that depend on the chosen physical distance metric. The proposed prediction algorithm is used for predictive image compression and performance comparisons with other predictive coding methods are presented.

Abstract: In a recent paper by Zhu and Zhao [1], an image encryption-compression scheme (IECS) based on hyper-chaos and Chinese remainder theorem (CRT) is proposed. This comment points out that in [1] there are some errors in two important algorithms and the performance of the proposed IECS is severely overestimated. It is shown that the IECS presented in paper [1] can be easily discredited and ipso facto cannot be used for compressing the plain image with a given compression ratio. Both theoretical analysis and experimental results are given to support our conclusion.

Abstract: Image data is always a major fraction of the huge data to be stored or transmitted. That is why researchers have been evolved in finding out different ways and techniques to increase compression rate and reduce information loss. This research investigated the improvement of JPEG compression algorithm by incorporating cubic spline interpolation (CSI) in the sampling stage and four different color spaces in the color space transformation stage. JPEG 1992 standard was considered and results were compared with previous works done by different researchers. The sampling and color space transformation stages of the JPEG algorithm were taken into consideration. In the color space transformation stage, two linear and non-uniform color spaces RGB and YIQ, and two uniform color spaces CIELAB and the CIECAM02 based uniform color space CAM02-UCS were incorporated and investigated. The sampling stage of JPEG contributes much to improve the compression rate at the cost of loss of some information. Current study incorporated cubic spline interpolation technique to reduce the information loss at this typical stage. The CIEDE2000 color difference formula, which is best correlated with the human visual perception, was used as metric to investigate performance of newly proposed improvements in JPEG algorithm for color image compression. The test results showed that the proposed modifications in the two stages of JPEG algorithm improved its performance in terms of compressibility and quality, and the difference in performance was statistically significant. Psychophysical experiments were also performed which validated the test results.

Abstract: 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.

Abstract: Gray values have always been altered in images collected by wireless multimedia sensor networks because of changes in light, weather and other conditions of monitored environment. In this case it may lead to the non-interest areas in the images to be misjudged as interest areas. If there are only a small number of pixels that have been affected in an image, the probability of misjudgment is smaller. However, if the affected pixels are massive, the difference method may judge many of non-interest areas as interest areas by mistake. This will increase the energy consumption of image compression process. Besides, it would not help to improve image quality. Therefore in the case of fixed reference frame, when there is an abrupt change in background environment, and only one concerning target in the image, we propose a method to predict the interest areas of current frame image by using the interest areas of history frames and the movement trend of the moving target. Binary-conversion based on the interest area and background area on the previous two historical frames. By using the connected component labeling algorithm based on run-length coding a single-target interest area can be determined. Predictor is determined according to coordinates’ extremes of two frames, and then the interest area of the current frame is predicted according to the previous frame and the predictor.

Abstract: CCD aerial camera is one of the important means of obtaining the image information on the ground, it is through the collection, archiving, and reading to achieved the images acquisition. As the very large amounts of data of the images, it takes a lot of time far more than analysis and processing when archiving and reading, so that not only difficult achieve real-time detection and processing, but also causing a waste of storage space. Therefore, the research of image compression and other processing technology has become important particularly.This paper use the wavelet coding to get images compression for the problem, and design the image processing system of aerial camera manipulator. This system designed by embedded modular, and ARINC 429 bus to achieve communications between the camera and the aircraft systems, make compression to the images which captured by the camera, and deal with the compressed image as stored, local zoom in and out, etc.

Abstract: Aiming at the problems of complicated convolution process of traditional wavelet transform and the unsatisfied effect of SPIHT algorithm for texture image compression, an improved SPIHT algorithm for texture image compression is proposed. At first, the texture image is decomposed into N order with the help of the lifting wavelet and the first-order high frequency sub-bands are decomposed into N-1 order by the lifting wavelet, and then the wavelet coefficients are encoded by the improved SPIHT algorithm. The improved SPIHT algorithm improved the process method of the wavelet coefficients in the low-frequency sub-bands and the detection method of some important coefficient in the L collection of the original SPIHT algorithm. Experiments show that the improved algorithm can retain the texture information of texture image more effectively and the quality of reconstructed image and peak signal to noise ratio are better than the original algorithm at the same rate. The effect is better especially at low rate, so the improved algorithm is an efficient compression method for texture image.

Abstract: In this paper, we proposed a layer segmentation based compression algorithm for gray images. Image textures and some high frequency noise are described in a high frequency layer while the coarse part of the image is included in the low frequency layer. A mixed dictionary and sparse coding is applied for high frequency layer coding and the low frequency layer is coded using the traditional wavelets based coding system. The results show that the proposed scheme achieves better rate-distortion performance compared with several competing compression system. Furthermore, in the sparse coding part, some edge-related atoms are added in the dictionary and a high sparseness factor is set for the edge blocks, making more accurate approximations for edges. We avoid further degradation of edges caused by compression.

Abstract: This article in detail on the basis of the algorithm of JPEG2000 standard upon, is put forward based on the FPGA embedded processor MicroBlaze and ADV212 compression chip with the combination of high speed, real time image compression scheme. Experiments show that the scheme can realize the data rate of 520 Mbps largest real-time compression of image data, when the image compression ratio is 53:1, reconstruction image PSNR value can reach 36 dB.

Abstract: An image compression denoising method based on median filter and wavelet transform is proposed in order to overcoming shortcomings of traditional methods of image processing in this paper. This method combined hardware parallelism with software technology is enable to achieve image compression denoising and take into account algorithm validation, and fast response of the system. An real-time image processing system is design by this method. Design and hardware implementation of fast median filtering algorithm based on EP1C12 FPGA chip is realized and software simulation of median filter and wavelet transform is done. The experimental results show that this system has advantages of fast response characteristic, less time consuming and high signal to noise ratio.