Image Compression Through Multi-Scale Learned Dictionaries

Data compression denotes the task of representing information in a compact way
so it can be stored and transmitted e ciently. In the case of lossy-compression,
this process may discard some information so that the reconstructed data is similar
enough to the original one, by compromising between accuracy and le size. Image
compression has a huge importance in a world where image resolution capabilities
of digital devices are constantly growing. Therefore , e cient and practical image
compression algorithms are of great concern where the goal is to produce higher
quality images with smaller le sizes.
JPEG2000 is currently the gold standard for image compression and just as its
former version, JPEG, is based on a sparsifying transformation (analytical dictio-
nary), by which the data is approximated by a few coe cients. On the other hand,
the K-SVD algorithm is a method that trains a dictionary to sparsely represent
real image examples. Since looking for a sparsifying transform is the core of any
popular compression algorithm, this concept holds considerable potential for data
compression.

Image Compression Through Multi-Scale Learned Dictionaries

 

Image Compression Through Multi-Scale Learned Dictionaries
Image Compression Through Multi-Scale
Learned Dictionaries
Collaboration:

Jeremias Sulam