A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fractal image compression: theory and application
Fractal image compression: theory and application
Embedded image coding using zerotrees of wavelet coefficients
IEEE Transactions on Signal Processing
Image coding using wavelet transform
IEEE Transactions on Image Processing
Classification Based Speed-Up Methods for Fractal Image Compression on Multicomputers
ParNum '99 Proceedings of the 4th International ACPC Conference Including Special Tracks on Parallel Numerics and Parallel Computing in Image Processing, Video Processing, and Multimedia: Parallel Computation
Hybrid image compression using fractal-wavelet prediction
ISP'06 Proceedings of the 5th WSEAS International Conference on Information Security and Privacy
A New Wavelet---Fractal Image Compression Method
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part I
Hi-index | 0.00 |
We describe an adaptive wavelet-based compression scheme for images. We decompose an image into a set of quantized wavelet coefficients and quantized wavelet subtrees. The vector codebook used for quantizing the subtrees is drawn from the image. Subtrees are quantized to contracted isometries of coarser scale subtrees. This codebook drawn from the contracted image is effective for quantizing locally smooth regions and locally straight edges. We prove that this self-quantization enables us to recover the fine scale wavelet coefficients of an image given its coarse scale coefficients. We show that this self-quantization algorithm is equivalent to a fractal image compression scheme when the wavelet basis is the Haar basis. The wavelet framework places fractal compression schemes in the context of existing wavelet subtree coding schemes. We obtain a simple convergence proof which strengthens existing fractal compression results considerably, derive an improved means of estimating the error incurred in decoding fractal compressed images, and describe a new reconstruction algorithm which requires O(N) operations for an N pixel image.