Efficient Quadtree based Fractal Image Coding Scheme in Wavelet Transform Domain *

This paper presents an efficient quadtree based fractal image coding scheme in wavelet transform domain based on the wavelet based theory of fractal image compression introduced by Davis. In the scheme, zerotrees of wavelet coefficients are used to reduce the number of domain blocks, which leads to lower bit cost required to represent the location information of fractal coding, and overall entropy constrained optimization is performed for the decision trees as well as for the sets of scalar quantizers and self quantizers of wavelet subtrees. Experiment results show that at the low bit rates, the proposed scheme gives about 1 dB improvement in PSNR over the reported results.

Medical Image Compression Using Wrapping Based Fast Discrete Curvelet Transform and Arithmetic Coding

Due to the development of CT (Computed Tomography), MRI (Magnetic Resonance Imaging), PET (Positron Emission Tomography), EBCT (Electron Beam Computed Tomography), SMRI (Stereotactic Magnetic Resonance Imaging), etc. has enhanced the distinguishing rate and scanning rate of the imaging equipments. The diagnosis and the process of getting useful information from the image are got by processing the medical images using the wavelet technique. Wavelet transform has increased the compression rate. Increasing the compression performance by minimizing the amount of image data in the medical images is a critical task. Crucial medical information like diagnosing diseases and their treatments is obtained by modern radiology techniques. Medical Imaging (MI) process is used to acquire that information. For lossy and lossless image compression, several techniques were developed. Image edges have limitations in capturing them if we make use of the extension of 1-D wavelet transform. This is because wavelet transform cannot effectively transform straight line discontinuities, as well geographic lines in natural images cannot be reconstructed in a proper manner if 1-D transform is used. Differently oriented image textures are coded well using Curvelet Transform. The Curvelet Transform is suitable for compressing medical images, which has more curvy portions. This paper describes a method for compression of various medical images using Fast Discrete Curvelet Transform based on wrapping technique. After transformation, the coefficients are quantized using vector quantization and coded using arithmetic encoding technique. The proposed method is tested on various medical images and the result demonstrates significant improvement in performance parameters like Peak Signal to Noise Ratio (PSNR) and Compression Ratio (CR).

METHOD OF VECTOR QUANTIZATION CODING USING RECTANGULAR TRANSFORM FOR IMAGE COMPRESSION

First of all a simple and practical rectangular transform is given,and then thevector quantization technique which is rapidly developing recently is introduced.We combinethe rectangular transform with vector quantization technique for image data compression.Thecombination cuts down the dimensions of vector coding.The size of the codebook can reasonablybe reduced.This method can reduce the computation complexity and pick up the vector codingprocess.Experiments using image processing system show that this method is very effective inthe field of image data compression.

3-D Wavelet Transform for Very Low Bit-Rate Video Coding

A new improved Goh's 3 D wavelet transform(WT) coding scheme is presented in this paper. The new scheme has great advantages including a simple code structure, low computation cost and good performance in PSNR, compression ratios and visual quality of reconstructions, when compared to the other existing 3 D WT coding methods and the 2 D WT based coding methods. The new 3 D WT coding scheme is suitable for very low bit rate video coding.

Second Generation Wavelet Applied to Lossless Compression Coding of Image

In this paper, the second generation wavelet transform is applied to image lossless coding, according to its characteristic of reversible integer wavelet transform. The second generation wavelet transform can provide higher compression ratio than Huffman coding while it reconstructs image without loss compared with the first generation wavelet transform. The experimental results show that the se cond generation wavelet transform can obtain excellent performance in medical image compression coding.

Low complexity joint source-channel decoding for transmission of wavelet compressed images

To utilize residual redundancy to reduce the error induced by fading channels and decrease the complexity of the field model to describe the probability structure for residual redundancy, a simplified statistical model for residual redundancy and a low complexity joint source-channel decoding（JSCD） algorithm are proposed. The complicated residual redundancy in wavelet compressed images is decomposed into several independent 1-D probability check equations composed of Markov chains and it is regarded as a natural channel code with a structure similar to the low density parity check （LDPC） code. A parallel sum-product （SP） and iterative JSCD algorithm is proposed. Simulation results show that the proposed JSCD algorithm can make full use of residual redundancy in different directions to correct errors and improve the peak signal noise ratio （PSNR） of the reconstructed image and reduce the complexity and delay of JSCD. The performance of JSCD is more robust than the traditional separated encoding system with arithmetic coding in the same data rate.

REMOTE SENSING IMAGE CODING METHOD COMBINING WAVELET TRANSFORM WITH CLASSIFIED VECTOR QUANTIZATION

A new remote sensing image coding scheme based on the wavelet transform and classified vector quantization (CVQ) is proposed. The original image is first decomposed into a hierarchy of 3 layers including 10 subimages by DWT. The lowest frequency subimage is compressed by scalar quantization and ADPCM. The high frequency subimages are compressed by CVQ to utilize the similarity among different resolutions while improving the edge quality and reducing computational complexity. The experimental results show that the proposed scheme has a better performance than JPEG, and a PSNR of reconstructed image is 31～33 dB with a rate of 0.2 bpp.

Wavelet-Based Mixed-Resolution Coding Approach Incorporating with SPT for the Stereo Image

With the advances of display technology, three-dimensional(3-D) imaging systems are becoming increasingly popular. One way of stimulating 3-D perception is to use stereo pairs, a pair of images of the same scene acquired from different perspectives. Since there is an inherent redundancy between the images of a stereo pairs, data compression algorithms should be employed to represent stereo pairs efficiently. The proposed techniques generally use block-based disparity compensation. In order to get the higher compression ratio, this paper employs the wavelet-based mixed-resolution coding technique to incorporate with SPT-based disparity-compensation to compress the stereo image data. The mixed-resolution coding is a perceptually justified technique that is achieved by presenting one eye with a low-resolution image and the other with a high-resolution image. Psychophysical experiments show that the stereo image pairs with one high-resolution image and one low-resolution image provide almost the same stereo depth to that of a stereo image with two high-resolution images. By combining the mixed-resolution coding and SPT-based disparity-compensation techniques, one reference (left) high-resolution image can be compressed by a hierarchical wavelet transform followed by vector quantization and Huffman encoder. After two level wavelet decompositions, for the low-resolution right image and low-resolution left image, subspace projection technique using the fixed block size disparity compensation estimation is used. At the decoder, the low-resolution right subimage is estimated using the disparity from the low-resolution left subimage. A full-size reconstruction is obtained by upsampling a factor of 4 and reconstructing with the synthesis low pass filter. Finally, experimental results are presented, which show that our scheme achieves a PSNR gain (about 0.92dB) as compared to the current block-based disparity compensation coding techniques.

An Adaptive Method of Image CompressionBased on Wavelet Analysis

Through research for image compression based on wavelet analysis in recent years, we put forward an adaptive wavelet decomposition strategy. Whether sub-images are to be decomposed or not are decided by their energy defined by certain criterion. Then we derive the adaptive wavelet decomposition tree (AWDT) and the way of adjustable compression ratio. According to the feature of AWDT, this paper also deals with the strategies which are used to handle different sub-images in the procedure of quantification and coding of the wavelet coefficients. Through experiments, not only the algorithm in the paper can adapt to various images, but also the quality of recovered image is improved though compression ratio is higher and adjustable. When their compression ratios are near, the quality of subjective vision and PSNR of the algorithm are better than those of JPEG algorithm.

Very Low Bit-Rate Video Coding Based on Wavelet Transform

Two video coding schemes based on wavelet transform achieving very low bit rate are presented in this paper. The first is a hybrid motion compensated wavelet transform(MC WT)system which behaves better at very low bit rates than the block DCT residual coder. The second is a new efficient coding system based on a simple frame differencing wavelet transform(FD WT)which performs well in both PSNR and visual quality with substantially reduced complexity.

Quantization-Based Robust Image Watermarking Using the Dual Tree Complex Wavelet Transform

Conventional quantization index modulation (QIM) watermarking uses the fixed quantization step size for the host signal.This scheme is not robust against geometric distortions and may lead to poor fidelity in some areas of content.Thus,we proposed a quantization-based image watermarking in the dual tree complex wavelet domain.We took advantages of the dual tree complex wavelets (perfect reconstruction,approximate shift invariance,and directional selectivity).For the case of watermark detecting,the probability of false alarm and probability of false negative were exploited and verified by simulation.Experimental results demonstrate that the proposed method is robust against JPEG compression,additive white Gaussian noise (AWGN),and some kinds of geometric attacks such as scaling,rotation,etc.

Entropy of images after wavelet transform

We studied the variation of image entropy before and after wavelet decomposition, the optimal number of wavelet decomposition layers, and the effect of wavelet bases and image frequency components on entropy. Numerous experiments were done on typical images to calculate (using Matlab) the entropy before and after wavelet transform. It was verified that, to obtain minimal entropy, a three-layer decomposition should be adopted rather than higher orders. The result achieved by using biorthogonal wavelet decomposition is better than that of the orthogonal wavelet decomposition. The results are not directly proportional to the vanishing moment, however.

Compressed Sensing Based on the Single Layer Wavelet Transform for Image Fusion

In this paper, a new method of combination single layer wavelet transform and compressive sensing is proposed for image fusion. In which only measured the high-pass wavelet coefficients of the image but preserved the low-pass wavelet coefficient. Then, fuse the low-pass wavelet coefficients and the measurements of high-pass wavelet coefficient with different schemes. For the reconstruction, by using the minimization of total variation algorithm (TV), high-pass wavelet coefficients could be recovered by the fused measurements. Finally, the fused image could be reconstructed by the inverse wavelet transform. The experiments show the proposed method provides promising fusion performance with a low computational complexity.

Authentication Method Using a Discrete Wavelet Transform for a Digital Moving Image

Recently, several digital watermarking techniques have been proposed for hiding data in the frequency domain of moving image files to protect their copyrights. However, in order to detect the water marking sufficiently after heavy compression, it is necessary to insert the watermarking with strong intensity into a moving image, and this results in visible deterioration of the moving image. We previously proposed an authentication method using a discrete wavelet transform for a digital static image file. In contrast to digital watermarking, no additional information is inserted into the original static image in the previously proposed method, and the image is authenticated by features extracted by the wavelet transform and characteristic coding. In the present study, we developed an authentication method for a moving image by using the previously proposed method for astatic image and a newly proposed method for selecting several frames in the moving image. No additional information is inserted into the original moving image by the newly proposed method or into the original static image by the previously proposed method. The experimental results show that the proposed method has a high tolerance of authentication to both compressions and vicious attacks.

Lossy-to-Lossless Compression of Hyperspectral Image Using the 3D Set Partitioned Embedded ZeroBlock Coding Algorithm

In this paper, we propose a three-dimensional Set Partitioned Embedded ZeroBlock Coding (3D SPEZBC) lossy-to-lossless compression algorithm for hyperspectral image which is an improved three-dimensional Embedded ZeroBlock Coding (3D EZBC) algorithm. The algorithm adopts the 3D integer wavelet packet transform proposed by Xiong et al. to decorrelate, the set-based partitioning zeroblock coding to process bitplane coding and the con-text-based adaptive arithmetic coding for further entropy coding. The theoretical analysis and experimental results demonstrate that 3D SPEZBC not only provides the same excellent compression performances as 3D EZBC, but also reduces the memory requirement compared with 3D EZBC. For achieving good coding performance, the diverse wave-let filters and unitary scaling factors are compared and evaluated, and the best choices were given. In comparison with several state-of-the-art wavelet coding algorithms, the proposed algorithm provides better compression performance and unsupervised classification accuracy.

SAR imagery coding based on blocking high frequency energy matching

A new fractal image compression algorithm based on high frequency energy (HFE) partitioning andmatched domain block searching is presented to code synthetic aperture radar (SAR) imagery. In the hybridcoding algorithm, the original SAR image is decomposed to low frequency components and high frequencycomponents by wavelet transform (WT). Then the coder uses HFE of block to partition and searchthe matched domain block for each range block to code the low frequency components. For the high frequencycomponents, a modified embedded zero-tree wavelet coding algorithm is applied. Experiment resultsshow that the proposed coder obtains about 0. 3dB gain when compared to the traditional fractal coderbased on the quadtree partition. Moreover, the subjective visual quality of the reconstructed SAR imageof the proposed coder outperforms that of the traditional fractal coders in the same compression ratio(CR).

A Novel Coding Method Based on Fuzzy Vector Quantization for Noised Image

In this paper a novel coding method based on fuzzy vector quantization for noised image with Gaussian white-noise pollution is presented. By restraining the high frequency subbands of wavelet image the noise is significantly removed and coded with fuzzy vector quantization. The experimental result shows that the method can not only achieve high compression ratio but also remove noise dramatically.

A hyperspectral image compression algorithm based on wavelet transformation and fractal composition (AWFC)

Starting with a fractal-based image-compression algorithm based on wavelet transformation for hyperspectral images, the authors were able to obtain more spectral bands with the help of of hyperspectral remote sensing. Because large amounts of data and limited bandwidth complicate the storage and transmission of data measured by TB-level bits, it is important to compress image data acquired by hyperspectral sensors such as MODIS, PHI, and OMIS; otherwise, conventional lossless compression algorithms cannot reach adequate compression ratios. Other loss-compression methods can reach high compression ratios but lack good image fidelity, especially for hyperspectral image data. Among the third generation of image compression algorithms, fractal image compression based on wavelet transformation is superior to traditional compression methods,because it has high compression ratios and good image fidelity, and requires less computing time. To keep the spectral dimension invariable, the authors compared the results of two compression algorithms based on the storage-file structures of BSQ and of BIP, and improved the HV and Quadtree partitioning and domain-range matching algorithms in order to accelerate their encode/decode efficiency. The authors' Hyperspectral Image Process and Analysis System (HIPAS) software used a VC++6.0 integrated development environment (IDE), with which good experimental results were obtained. Possible modifications of the algorithm and limitations of the method are also discussed.

SFCVQ and EZW coding method based on Karhunen-Loeve transformation and integer wavelet transformation

A new hyperspectral image compression method of spectral feature classification vector quantization （SFCVQ） and embedded zero-tree of wavelet （EZW） based on Karhunen-Loeve transformation （KLT） and integer wavelet transformation is represented. In comparison with the other methods, this method not only keeps the characteristics of high compression ratio and easy real-time transmission, but also has the advantage of high computation speed. After lifting based integer wavelet and SFCVQ coding are intro- duced, a system of nearly lossless compression of hyperspectral images is designed. KLT is used to remove the correlation of spectral redundancy as one-dimensional （1D） linear transform, and SFCVQ coding is applied to enhance compression ratio. The two-dimensional （2D） integer wavelet transformation is adopted for the decorrelation of 2D spatial redundancy. EZW coding method is applied to compress data in wavelet domain. Experimental results show that in comparison with the method of wavelet SFCVQ （WSFCVQ）, the method of improved BiBlock zero tree coding （IBBZTC） and the method of feature spectral vector quantization （FSVQ）, the peak signal-to-noise ratio （PSNR） of this method can enhance over 9 dB, and the total compression performance is improved greatly.

Low complexity DCT-based distributed source coding with Gray code for hyperspectral images

To compress hyperspectral images, a low complexity discrete cosine transform （DCT）-based distributed source coding （DSC） scheme with Gray code is proposed. Unlike most of the existing DSC schemes, which utilize transform in spatial domain, the proposed algorithm applies transform in spectral domain. Set-partitioning-based approach is applied to reorganize DCT coefficients into waveletlike tree structure and extract the sign, refinement, and significance bitplanes. The extracted refinement bits are Gray encoded. Because of the dependency along the line dimension of hyperspectral images, low density paritycheck-（LDPC）-based Slepian-Wolf coder is adopted to implement the DSC strategy. Experimental results on airborne visible/infrared imaging spectrometer （AVIRIS） dataset show that the proposed paradigm achieves up to 6 dB improvement over DSC-based coders which apply transform in spatial domain, with significantly reduced computational complexity and memory storage.