Performance Evaluation of Hybrid Coding of Images Using Wavelet Transform and Predictive Coding

Image compression techniques are necessary for the storage of huge amounts of digital images using reasonable amounts of space, and for their transmission with limited bandwidth. Several techniques such as predictive coding, transform coding, subband coding, wavelet coding, and vector quantization have been used in image coding. While each technique has some advantages, most practical systems use hybrid techniques which incorporate more than one scheme. They combine the advantages of the individual schemes and enhance the coding effectiveness. This paper proposes and evaluates a hybrid coding scheme for images using wavelet transforms and predictive coding. The performance evaluation is done using a variety of different parameters such as kinds of wavelets, decomposition levels, types of quantizers, predictor coefficients, and quantization levels. The results of evaluation are presented

Applications of wavelet-based compression to multidimensional Earth science data

A data compression algorithm involving vector quantization (VQ) and the discrete wavelet transform (DWT) is applied to two different types of multidimensional digital earth-science data. The algorithms (WVQ) is optimized for each particular application through an optimization procedure that assigns VQ parameters to the wavelet transform subbands subject to constraints on compression ratio and encoding complexity. Preliminary results of compressing global ocean model data generated on a Thinking Machines CM-200 supercomputer are presented. The WVQ scheme is used in both a predictive and nonpredictive mode. Parameters generated by the optimization algorithm are reported, as are signal-to-noise (SNR) measurements of actual quantized data. The problem of extrapolating hydrodynamic variables across the continental landmasses in order to compute the DWT on a rectangular grid is discussed. Results are also presented for compressing Landsat TM 7-band data using the WVQ scheme. The formulation of the optimization problem is presented along with SNR measurements of actual quantized data. Postprocessing applications are considered in which the seven spectral bands are clustered into 256 clusters using a k-means algorithm and analyzed using the Los Alamos multispectral data analysis program, SPECTRUM, both before and after being compressed using the WVQ program

Parental finite state vector quantizer and vector wavelet transform-linear predictive coding.

by Lam Chi Wah.Thesis submitted in: December 1997.Thesis (M.Phil.)--Chinese University of Hong Kong, 1998.Includes bibliographical references (leaves 89-91).Abstract also in Chinese.Chapter Chapter 1 --- Introduction to Data Compression and Image Coding --- p.1Chapter 1.1 --- Introduction --- p.1Chapter 1.2 --- Fundamental Principle of Data Compression --- p.2Chapter 1.3 --- Some Data Compression Algorithms --- p.3Chapter 1.4 --- Image Coding Overview --- p.4Chapter 1.5 --- Image Transformation --- p.5Chapter 1.6 --- Quantization --- p.7Chapter 1.7 --- Lossless Coding --- p.8Chapter Chapter 2 --- Subband Coding and Wavelet Transform --- p.9Chapter 2.1 --- Subband Coding Principle --- p.9Chapter 2.2 --- Perfect Reconstruction --- p.11Chapter 2.3 --- Multi-Channel System --- p.13Chapter 2.4 --- Discrete Wavelet Transform --- p.13Chapter Chapter 3 --- Vector Quantization (VQ) --- p.16Chapter 3.1 --- Introduction --- p.16Chapter 3.2 --- Basic Vector Quantization Procedure --- p.17Chapter 3.3 --- Codebook Searching and the LBG Algorithm --- p.18Chapter 3.3.1 --- Codebook --- p.18Chapter 3.3.2 --- LBG Algorithm --- p.19Chapter 3.4 --- Problem of VQ and Variations of VQ --- p.21Chapter 3.4.1 --- Classified VQ (CVQ) --- p.22Chapter 3.4.2 --- Finite State VQ (FSVQ) --- p.23Chapter 3.5 --- Vector Quantization on Wavelet Coefficients --- p.24Chapter Chapter 4 --- Vector Wavelet Transform-Linear Predictor Coding --- p.26Chapter 4.1 --- Image Coding Using Wavelet Transform with Vector Quantization --- p.26Chapter 4.1.1 --- Future Standard --- p.26Chapter 4.1.2 --- Drawback of DCT --- p.27Chapter 4.1.3 --- "Wavelet Coding and VQ, the Future Trend" --- p.28Chapter 4.2 --- Mismatch between Scalar Transformation and VQ --- p.29Chapter 4.3 --- Vector Wavelet Transform (VWT) --- p.30Chapter 4.4 --- Example of Vector Wavelet Transform --- p.34Chapter 4.5 --- Vector Wavelet Transform - Linear Predictive Coding (VWT-LPC) --- p.36Chapter 4.6 --- An Example of VWT-LPC --- p.38Chapter Chapter 5 --- Vector Quantizaton with Inter-band Bit Allocation (IBBA) --- p.40Chapter 5.1 --- Bit Allocation Problem --- p.40Chapter 5.2 --- Bit Allocation for Wavelet Subband Vector Quantizer --- p.42Chapter 5.2.1 --- Multiple Codebooks --- p.42Chapter 5.2.2 --- Inter-band Bit Allocation (IBBA) --- p.42Chapter Chapter 6 --- Parental Finite State Vector Quantizers (PFSVQ) --- p.45Chapter 6.1 --- Introduction --- p.45Chapter 6.2 --- Parent-Child Relationship Between Subbands --- p.46Chapter 6.3 --- Wavelet Subband Vector Structures for VQ --- p.48Chapter 6.3.1 --- VQ on Separate Bands --- p.48Chapter 6.3.2 --- InterBand Information for Intraband Vectors --- p.49Chapter 6.3.3 --- Cross band Vector Methods --- p.50Chapter 6.4 --- Parental Finite State Vector Quantization Algorithms --- p.52Chapter 6.4.1 --- Scheme I: Parental Finite State VQ with Parent Index Equals Child Class Number --- p.52Chapter 6.4.2 --- Scheme II: Parental Finite State VQ with Parent Index Larger than Child Class Number --- p.55Chapter Chapter 7 --- Simulation Result --- p.58Chapter 7.1 --- Introduction --- p.58Chapter 7.2 --- Simulation Result of Vector Wavelet Transform (VWT) --- p.59Chapter 7.3 --- Simulation Result of Vector Wavelet Transform - Linear Predictive Coding (VWT-LPC) --- p.61Chapter 7.3.1 --- First Test --- p.61Chapter 7.3.2 --- Second Test --- p.61Chapter 7.3.3 --- Third Test --- p.61Chapter 7.4 --- Simulation Result of Vector Quantization Using Inter-band Bit Allocation (IBBA) --- p.62Chapter 7.5 --- Simulation Result of Parental Finite State Vector Quantizers (PFSVQ) --- p.63Chapter Chapter 8 --- Conclusion --- p.86REFERENCE --- p.8

Wavelet-based video codec using human visual system coefficients for 3G mobiles

A new wavelet based video codec that uses human visual system coefficients is presented. In INTRA mode of operation, wavelet transform is used to split the input frame into a number of subbands. Human Visual system coefficients are designed for handheld videophone devices and used to regulate the quantization stepsize in the pixel quantization of the high frequency subbands’ coefficients. The quantized coefficients are coded using quadtreecoding scheme. In the INTER mode of operation, the displaced frame difference is generated and a wavelet transform decorrelates it into a number of subbands. These subbands are coded using adaptive vector quantization scheme. Results indicate a significant improvement in frame quality compared to motion JPEG200

MDL Denoising Revisited

We refine and extend an earlier MDL denoising criterion for wavelet-based denoising. We start by showing that the denoising problem can be reformulated as a clustering problem, where the goal is to obtain separate clusters for informative and non-informative wavelet coefficients, respectively. This suggests two refinements, adding a code-length for the model index, and extending the model in order to account for subband-dependent coefficient distributions. A third refinement is derivation of soft thresholding inspired by predictive universal coding with weighted mixtures. We propose a practical method incorporating all three refinements, which is shown to achieve good performance and robustness in denoising both artificial and natural signals.Comment: Submitted to IEEE Transactions on Information Theory, June 200

An Iterative Detection Aided Unequal Error Protection Wavelet Video Scheme Using Irregular Convolutional Codes

A wavelet-based videophone scheme proposed, where the video bits are Unequal Error Protection (UEP) using Irregular Convolutional Codes (IRCCs). The proposed system uses Adaptive Arithmetic Coding (AAC) for encoding the motion vectors and individual wavelet subband coefficients. The turbo equalized IRCC-aided videophone scheme is capable of attaining a near unimpaired video quality for channel Signal-to-Noise Ratios (SNRs) in excess of about 4.5dB over a five-path dispersive AWGN channel

A family of stereoscopic image compression algorithms using wavelet transforms

With the standardization of JPEG-2000, wavelet-based image and video compression technologies are gradually replacing the popular DCT-based methods. In parallel to this, recent developments in autostereoscopic display technology is now threatening to revolutionize the way in which consumers are used to enjoying the traditional 2D display based electronic media such as television, computer and movies. However, due to the two-fold bandwidth/storage space requirement of stereoscopic imaging, an essential requirement of a stereo imaging system is efficient data compression. In this thesis, seven wavelet-based stereo image compression algorithms are proposed, to take advantage of the higher data compaction capability and better flexibility of wavelets. In the proposed CODEC I, block-based disparity estimation/compensation (DE/DC) is performed in pixel domain. However, this results in an inefficiency when DWT is applied on the whole predictive error image that results from the DE process. This is because of the existence of artificial block boundaries between error blocks in the predictive error image. To overcome this problem, in the remaining proposed CODECs, DE/DC is performed in the wavelet domain. Due to the multiresolution nature of the wavelet domain, two methods of disparity estimation and compensation have been proposed. The first method is performing DEJDC in each subband of the lowest/coarsest resolution level and then propagating the disparity vectors obtained to the corresponding subbands of higher/finer resolution. Note that DE is not performed in every subband due to the high overhead bits that could be required for the coding of disparity vectors of all subbands. This method is being used in CODEC II. In the second method, DEJDC is performed m the wavelet-block domain. This enables disparity estimation to be performed m all subbands simultaneously without increasing the overhead bits required for the coding disparity vectors. This method is used by CODEC III. However, performing disparity estimation/compensation in all subbands would result in a significant improvement of CODEC III. To further improve the performance of CODEC ill, pioneering wavelet-block search technique is implemented in CODEC IV. The pioneering wavelet-block search technique enables the right/predicted image to be reconstructed at the decoder end without the need of transmitting the disparity vectors. In proposed CODEC V, pioneering block search is performed in all subbands of DWT decomposition which results in an improvement of its performance. Further, the CODEC IV and V are able to perform at very low bit rates(< 0.15 bpp). In CODEC VI and CODEC VII, Overlapped Block Disparity Compensation (OBDC) is used with & without the need of coding disparity vector. Our experiment results showed that no significant coding gains could be obtained for these CODECs over CODEC IV & V. All proposed CODECs m this thesis are wavelet-based stereo image coding algorithms that maximise the flexibility and benefits offered by wavelet transform technology when applied to stereo imaging. In addition the use of a baseline-JPEG coding architecture would enable the easy adaptation of the proposed algorithms within systems originally built for DCT-based coding. This is an important feature that would be useful during an era where DCT-based technology is only slowly being phased out to give way for DWT based compression technology. In addition, this thesis proposed a stereo image coding algorithm that uses JPEG-2000 technology as the basic compression engine. The proposed CODEC, named RASTER is a rate scalable stereo image CODEC that has a unique ability to preserve the image quality at binocular depth boundaries, which is an important requirement in the design of stereo image CODEC. The experimental results have shown that the proposed CODEC is able to achieve PSNR gains of up to 3.7 dB as compared to directly transmitting the right frame using JPEG-2000