Image Compression

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A Fractal Image Compression Method Based on Multi-Wavelet

A Fractal Image Compression Method Based on Multi-Wavelet

The image compression technology is a technique to use as few bits as possible to express the image signal from the information source to loweras much resource consumption such as the frequency bandwidth occupied by the image data, the storage space and the transmission time as possible for the sake of the transmission and storage of image signal. In fact, there exists strong correlation between the image pixels and such correlation has brought plenty of redundant information to the image, which makes image compression possible [1]. As a new image compression algorithm developed in the past decade, fractal image compression method attaches great importance to digging the self-similarity in most images and realizes the coding of an image with complicated visual characteristics on the surface via limited coefficients by using the iterated function system and some simple iteration rules. By using these rules, the decoder can realize the iterative decoding of the original image, therefore, the fractal image compression algorithm can achieve a highercompression ratio than other image compression algorithms [2].
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Energy Efficiency of Image Compression for Virtual View Image over Wireless Visual Sensor Network.

Energy Efficiency of Image Compression for Virtual View Image over Wireless Visual Sensor Network.

Based on the discussion of the above studies, the main contribution of this research is to give solution for energy saving in distributed processing. The distributed processing by means of image compression and image selection which have different information to the infor- mation already stored in each visual sensor node. The use of energy on each process is measured. The quality of virtual view image is improved and compared to the result in [10]. WVSN platform XScale PXA 271 processor, visual sensor SOC OV 7670, and sensor board with Linux Embedded OS [11]–[13] are used in this research. This paper is organized as follows. Section II describes proposed scheme and system design. Section III presents testing, measurement and analysis of the system. Finally, section IV concludes the research.
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Backpropagation Neural Network Implementation for Medical Image Compression

Backpropagation Neural Network Implementation for Medical Image Compression

Copyright © 2013 Kamil Dimililer. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Medical images require compression, before transmission or storage, due to constrained bandwidth and storage capacity. An ideal image compression system must yield high-quality compressed image with high compression ratio. In this paper, Haar wavelet transform and discrete cosine transform are considered and a neural network is trained to relate the X-ray image contents to their ideal compression method and their optimum compression ratio.
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An Efficient 2x2 Tchebichef Moments for Mobile Image Compression

An Efficient 2x2 Tchebichef Moments for Mobile Image Compression

lot of images back and forth. Image compression is needed to reduce transmission payload at the expense of lower quality. At the same time, mobile devices are only expected to be equipped with lower computing power and storage. They need an eicient compression scheme especially for small images. The standard PEG using discrete Cosine transform is a popular lossy image compression. Alternatively, this paper introduces 2x2 Tchebichef moments transform for the eicient image compression. In the previous research, larger sub-block discrete Tchebichef moments have been used extensively for image compression. The comparisons between PEG compression and 2x2 Tchebichef moments image compressions shall be done. The preliminary experiment results show that 2x2 Tchebichef moments transform has the potential to easily perform better than PEG image compression. The 2x2 Tchebichef moments provides an eicient and compact support for image compression.
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ADAPTIVE TCHEBICHEF MOMENT TRANSFORM IMAGE COMPRESSION USING PSYCHOVISUAL MODEL

ADAPTIVE TCHEBICHEF MOMENT TRANSFORM IMAGE COMPRESSION USING PSYCHOVISUAL MODEL

An extension of the standard JPEG image compression known as JPEG-3 allows rescaling of the quantization matrix to achieve a certain image output quality. Recently, Tchebichef Moment Transform (TMT) has been introduced in the field of image compression. TMT has been shown to perform better than the standard JPEG image compression. This study presents an adaptive TMT image compression. This task is obtained by generating custom quantization tables for low, medium and high image output quality levels based on a psychovisual model. A psychovisual model is developed to approximate visual threshold on Tchebichef moment from image reconstruction error. The contribution of each moment will be investigated and analyzed in a quantitative experiment. The sensitivity of TMT basis functions can be measured by evaluating their contributions to image reconstruction for each moment order. The psychovisual threshold model allows a developer to design several custom TMT quantization tables for a user to choose from according to his or her target output preference. Consequently, these quantization tables produce lower average bit length of Huffman code while still retaining higher image quality than the extended JPEG scaling scheme.
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Psychovisual Threshold on Large Tchebichef Moment for Image Compression

Psychovisual Threshold on Large Tchebichef Moment for Image Compression

moments for image compression. The psychovisual threshold is practically the best measure on an optimal amount of frequency image signals to be lossy encoded. This quantitative experiment shows the critical role of psychovisual threshold as a basic primitive to generate quantization table in image compression. The finer large Tchebichef quantization tables can then be generated from the psychovisual threshold. The psychovisual threshold on large Tchebichef moment has made significant improvement in the Tchebichef image compression performance. The smoother large Tchebichef quantization tables from the psychovisual threshold produce better visual quality of the output image and smaller average bit length of Huffman code than standard JPEG image compression. At the same time, the psychovisual threshold on large Tchebichef moments manages to overcome the blocking effect which often occurs in standard JPEG image compression. This psychovisual threshold is designed to support practical image compression for high fidelity digital images in the near future. Acknowledgements. The authors would like to express a very special thanks to Ministry of Education, Malaysia for providing financial support by Fundamental Research Grant Scheme (FRGS/2012/FTMK/SG05/03/1/F00141).
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An Image Compression Scheme Based on Fuzzy Neural Network

An Image Compression Scheme Based on Fuzzy Neural Network

Image compression technology refers to the method using as less as possible bits to represent the image signal from the signal source and possibly reducing such resource consumption occupied by the image data as the frequency bandwidth, storage space and transmission time etc., so as to transmit and store the image signals [1]. The main purpose of image compression is to eliminate the redundant information of images, including encoding redundancy, redundancy between pixels and psychological visual redundancy information [2]. In past decades, studies on the image compression have obtained the rapid development, and many effective algorithms have occurred,and compression standards such as JPEG, JPEG2000 and MPEG have been formed. In order to further compress images, we can start from two aspects: one is the use of vision system feature with the human eye as "final consumer" of the image information. The image compression based on human visual characteristics increasingly becomes the feature for people to study, and because of the complexity of the visual system, at present, there still exist a lot of unknown areas to be explored in this field. The second is the development of new compression tools and more intelligent algorithms. Owing to the excellent performance, there still exist a lot of room for the artificial neural network to be applied in the field of image compression [3].
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Energy efficiency of image compression for virtual view image over wireless visual sensor network.

Energy efficiency of image compression for virtual view image over wireless visual sensor network.

Abstract — Many applications use virtual view to see objects or sceneries in certain directions where there is no angle of visual sensor viewing the directions. Generating indirect virtual view images also save visual sensor node in a network. In addition visual sensors have abilities to give more information related to virtual view images. In fact, generating virtual view images using Wireless Visual Sensor Network is a challenging task, due to the limitation of energy, computation, and bandwidth. Therefore this paper proposes using combination of camera selection method with parameters optimization of JPEG 2000 image com- pression and image transmission strategy applying mutual information criteria. The camera selection method based on the smallest value of disparity was adopted to reduce transmission of number of images. In each node of visual sensor, the optimization of JPEG 2000 image compression using parameters selection such as DWT level and bit rate was applied to reduce the size of the images. However image quality in PSNR has to be preserved. By implementing the sensor selection, the image compression, and the mutual information strategic, the study results that the total energy required to send one compressed image can be reduced to 10.27% of energy consumption of uncompressed image. Thus the image will be faster delivered.
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Efficient JPEG 2000 Image Compression Scheme for Multihop Wireless Networks

Efficient JPEG 2000 Image Compression Scheme for Multihop Wireless Networks

In this paper, we have studied the problems of distributed image compression algorithm and its application in WSNs. The distributed image compression algorithm presented in this paper offers much flexibility at different process levels. These flexibilities are considered as dynamic parameters during the system to adapt the communication process. We have focused our study on the design and evaluation of distributed scheme depending on the operating parameters at different process levels. We have explained the impact of these parameters on the WSNs operations. Adopting the proposed technique, should reduce required memory, and minimize energy consumption. In addition, the adopted approach should minimize significantly computational energy of the nodes located next to the source (data is in an uncompressed form) by reducing the number of arithmetic operations and therefore, extends the overall network lifetime. The future research works must be focused on multipath routing which may enhance the performance of distributed image compression.
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Integrating a Smooth Psychovisual Threshold into an Adaptive JPEG Image Compression

Integrating a Smooth Psychovisual Threshold into an Adaptive JPEG Image Compression

The sensitivity of the human eye can be fully explored and investigated via qualitative experiments. However, it is too expensive and costly to conduct such qualitative experiments. Several approaches have been conducted to investigate the psychovisual models such as human visual weighting [8] and psychovisual model based on preserving downsampling and upsampling [9]. In general, a psychovisual model has been designed based on the understanding of brain theory and neuroscience [10]. This paper will explore on the concept of adaptive psychovisual threshold to provide custom quantization tables on JPEG image compression. The concept of the psychovisual error threshold can be obtained from quantitative experiments by evaluating the just noticeable difference of the compressed image from the original image at various frequency orders [11][12].
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An Optimal Tchebichef Moment Quantization using Psychovisual Threshold for Image Compression

An Optimal Tchebichef Moment Quantization using Psychovisual Threshold for Image Compression

Colour image carries a certain amount of perceptual redundancy for the human eyes. The human eye is capable of perceiving various levels of colours. The sensitivity of human eye is useful for perceptual visual quality image in image compression. The visual sensitivity of the colour image in terms of image compression can be measured by a psychovisual threshold to generate the quantization tables. This paper will investigate a psychovisual threshold level for Tchebichef moment transform (TMT) from the contribution of its moments. In this paper presents a new technique to generate quantization table for TMT image compression based on psychovisual error threshold. The experimental results show that these new finer quantization tables based on psychovisual threshold for TMT provides better quality image and lower average bit length of Huffman code than previously proposed TMT quantization.
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A Comparation Between SVD and SSVD Method on Image Compression

A Comparation Between SVD and SSVD Method on Image Compression

Data compression is the process of converting an input data into other data which has a smaller size. The data can be a file on a computer or a buffer in the computer’s memory. Compression is useful because it helps reduce the consumption of resources such as data space or transmission capacity. The use of singular value decomposition (SVD) in image compression has been widely studied. If the image, when considered as a matrix, has low rank, or can be approximated sufficiently well by a matrix of low rank, then SVD can be used to find this approximation. That is, by not include some elements of the image, the approximation has been able to represents the original image. This thesis presents a variation for SVD image compression technique proposed by Ranade et al. called SSVD. This variation can be viewed as a preprocessing step in which the input image is permuted by independent data permutation after it is fed to the standard SVD algorithm. Likewise, this decompression algorithm can be viewed as the standard SVD algorithm followed by a postprocessing step which applies the inverse permutation. On experimenting with some standard images, SSVD performs substantially better than SVD. This thesis also presents experiment evidence with other simulated images, which appears that SSVD isn’t better than SVD.
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A Novel Psychovisual Threshold on Large DCT for Image Compression

A Novel Psychovisual Threshold on Large DCT for Image Compression

he DCT coeicients from a large image block have been greatly discounted by quantization tables in image compression. An optimal amount of DCT coeicients is investigated by reconstruction error and average bit length of Hufman code. he efect of incrementing DCT coeicient has been explored from this experiment. he average recon- struction error from incrementing DCT coeicients is mainly concentrated in the low frequency order of the image signals. he new 256 × 256 quantization table from the psychovi- sual threshold produces a lower average bit length of Hufman code in image compression as shown in Table 4. At the same time, the compressed output images produce a better quality image reconstruction than the regular 8 × 8 default JPEG quantization tables as listed in Table 5. he new design on quantization tables from psychovisual threshold performs better by producing higher quality in image reconstruction at lower average bit length of Hufman code.
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Combinatorial Transforms : Applications in Lossless Image Compression

Combinatorial Transforms : Applications in Lossless Image Compression

Common image compression standards are usually based on fre- quency transform such as Discrete Cosine Transform. We present a different approach for lossless image compression, which is based on a combinatorial transform. The main transform is Burrows Wheeler Transform (BWT) which tends to reorder symbols according to their following context. It becomes one of promising compression approach based on context modeling. BWT was initially applied for text com- pression software such as BZIP2 ; nevertheless it has been recently ap- plied to the image compression field. Compression schemes based on the Burrows Wheeler Transform have been usually lossless; therefore we implement this algorithm in medical imaging in order to recon- struct every bit. Many variants of the three stages which form the original compression scheme can be found in the literature. We pro- pose an analysis of the latest methods and the impact of their associa- tion and present an alternative compression scheme with a significant improvement over the current standards such as JPEG and JPEG2000 .
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Image Compression Using the Discrete Cosine Transform

Image Compression Using the Discrete Cosine Transform

All three of these standards employ a basic technique known as the discrete cosine transform (DCT). Developed by Ahmed, Natarajan, and Rao [1974], the DCT is a close relative of the discrete Fourier transform (DFT). Its application to image compression was pioneered by Chen and Pratt [1984]. In this article, I will develop some simple functions to compute the DCT and show how it is used for image compression. We have used these functions in our laboratory to explore methods of optimizing image compression for the human viewer, using information about the human visual system [Watson 1993]. The goal of this paper is to illustrate the use of Mathematica in image processing and to provide the reader with the basic tools for further exploration of this subject.
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An Adaptive JPEG Image Compression Using Psychovisual Model

An Adaptive JPEG Image Compression Using Psychovisual Model

An adaptive JPEG image compression using psychovisual threshold has been investigated. The performance of the proposed adaptive JPEG image compression based on psychovisual threshold and the extended JPEG image compression using the quantization scaling factor have been compared. An adaptive quantization allows an image to be compressed at a better visual quality thus improving the compression rate. The experimental results of the new quantization tables based on psychovisual threshold produce an optimal quantization tables with higher quality image reconstruction at lower average bit length of Huffman code. This approach can be the next generation model to generate quantization tables for JPEG image compression especially on high compression rate.
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An efficient psychovisual threshold technique in image compression.

An efficient psychovisual threshold technique in image compression.

5.6.3f The original Lena image (i) and the differentiate visual outputs of bit allocation from the original Lena image with multiply by a factor of 16 on the local blocks of 8 coefficients (ii), 16 coefficients (iii) and 32 coefficients (iv) on 256×256 DCT zoomed in to 800%

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Pembuatan Aplikasi Kompresi Image dengan Metode Fast Discrete Cosine Transform

Pembuatan Aplikasi Kompresi Image dengan Metode Fast Discrete Cosine Transform

Programs that are created using lossy image compression and after the test successfully met the standard JPEG, fast in terms of computation and produces good output. This program uses computational algorithms and optimization fDCT other so that the computing speed is faster than usual. Tests conducted by IJG and the results made programs more quickly in terms of computation, while the resulting quality of compression is inversely proportional to the desired data.

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Performance Analysis Of Interpolations Method For Mobile Image Sharing Compression.

Performance Analysis Of Interpolations Method For Mobile Image Sharing Compression.

Nowadays, virtual meeting using mobile apps has become the most popular way to socialize through all over the world. With the advancement of image sharing function in the mobile social apps like Whatspp, WeChat and etc, the virtual meeting seems almost the same as the actual face to face meeting. However, because of the internet speed limitation, the image file is usually compressed to a smaller file size before the image is transmitted to the apps server. This process is important to ensure that the real-time experience between the users of the social apps. Due to the pre- transmission image compression, the quality of the receive image is quite degraded than the original image sent by the originators especially in terms of resolutions and image quality.
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Penginderaan Visual untuk berbagai keperluan

Penginderaan Visual untuk berbagai keperluan

architecture facilitates rapid development of machine vision, medical imaging, and image analysis applications. HALCON provides outstanding performance and a comprehensive support of multi-core platforms, MMX, and SSE2. It serves all industries by a library of more than 1400 operators for blob analysis,

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