Second-generation image coding: an overview
ACM Computing Surveys (CSUR)
Parallel Implementation of Multidimensional Transforms without Interprocessor Communication
IEEE Transactions on Computers
A nonseparable VLSI architecture for two-dimensional discrete periodized wavelet transform
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Discriminant Waveletfaces and Nearest Feature Classifiers for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalized Low Rank Approximations of Matrices
Machine Learning
Closed-loop method to improve image PSNR in pyramidal CMAC networks
International Journal of Computer Applications in Technology
Palmprint Recognition by Applying Wavelet Subband Representation and Kernel PCA
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
A Neuro Fuzzy Model for Image Compression in Wavelet Domain
ICISP '08 Proceedings of the 3rd international conference on Image and Signal Processing
Image compression using subband wavelet decomposition and DCT-based quantization
MUSP'06 Proceedings of the 6th WSEAS international conference on Multimedia systems & signal processing
A PCA-wavelet based compression for distance learning images
MUSP'06 Proceedings of the 6th WSEAS international conference on Multimedia systems & signal processing
Temporal video compression by discrete wavelet transform
SIP'06 Proceedings of the 5th WSEAS international conference on Signal processing
A DCT-SVD based robust watermarking scheme for grayscale image
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
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Schemes for image compression of black-and-white images based on the wavelet transform are presented. The multiresolution nature of the discrete wavelet transform is proven as a powerful tool to represent images decomposed along the vertical and horizontal directions using the pyramidal multiresolution scheme. The wavelet transform decomposes the image into a set of subimages called shapes with different resolutions corresponding to different frequency bands. Hence, different allocations are tested, assuming that details at high resolution and diagonal directions are less visible to the human eye. The resultant coefficients are vector quantized (VQ) using the LGB algorithm. By using an error correction method that approximates the reconstructed coefficients quantization error, we minimize distortion for a given compression rate at low computational cost. Several compression techniques are tested. In the first experiment, several 512×512 images are trained together and common table codes created. Using these tables, the training sequence black-and-white images achieve a compression ratio of 60-65 and a PSNR of 30-33. To investigate the compression on images not part of the training set, many 480×480 images of uncalibrated faces are trained together and yield global tables code. Images of faces outside the training set are compressed and reconstructed using the resulting tables. The compression ratio is 40; PSNRs are 30-36. Images from the training set have similar compression values and quality. Finally, another compression method based on the end vector bit allocation is examined