Using Singular Value Decomposition to perform Lossy Compression of Images
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Updated
Sep 26, 2017 - HTML
Using Singular Value Decomposition to perform Lossy Compression of Images
Various algorithms for lossy and lossless compression
Java implementation for some lossy and lossless compression algorithms (LZ77, LZ78, LZW, Non-uniform quantization and Standard Huffman)
Contains Lossless and Lossy compression techniques with output files. Use PSNR for quality measurement.
Test file to check Fortran bindings to the SZ3 library for lossy compression
Some algorithms for lossy compression of images
fortran version of zfp C code example - simple compressor
NSF CSSI Project "ROCCI: Integrated Cyberinfrastructure for In Situ Lossy Compression Optimization Based on Post Hoc Analysis Requirements"
PhD thesis 2015, Multiresolution Time Series Database Model
The 1st International Workshop on Big Data Reduction
Implemented a naive indexer for Reuters21578. Implemented single-term query processing. Implmented and compared results of lossy dictionary compression
Data Mining and Predictive Analyst - Fall 2021 - Predictive Modeling, Loss Reduction, Optimization Algorithms, Regularization (Ridge, Lasso, Elastic Net), Dimensionality Reduction, Ensemble Models, Bootstrap Aggregation, Random Forests, Support Vector Machines (SVM), Neural Networks.
ImageCodec
“Smooth signal functions” in Haskell
image compression simulation using EVD(Eigen Value Decomposition)
MECO: Multi-objective Evolutionary Compression
Compression using various algorithms and Comparision
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