Data Compression Algorithm for Audio and Image using Feature Extraction

Abstract

We aim to achieve the highest data compression ratio in a lossy scenario while still maintaining the original image or audio files characteristics and resolution/bitrate. For this we would run feature extraction on chunks of the data and store them in a database with a specific hash as a key. This hash will be stored in the file and the full data later reconstructed from the database. The database will be created by training on a vast range of data and storing only the most common chunks encountered by hash. The compression ratio achieved for image it is 0.01 over standard raw input data.