Compressed Sensing vs JPEG
From iis-projects
Short Description
Compressed Sensing (CS) is a signal processing scheme that aims at combining signal acquisition and data compression in one single step. CS can be implemented very efficiently in digital logic, and the encoding (or compression) step can be performed with very little hardware (and power) effort. Instead, the reconstruction (or decompression) step requires fairly sophisticated algorithms. Understood as data compression/decompression strategy, CS is a highly assymmetric CODEC making its application in wireless telemetry applications attractive.
In this project, we are interested in applying CS to low resolution image compression and compare it to well established strategies, such as JPEG.
JPEG is a widely used in practice, and is based on transform coding using the DCT (discrete cosine transform), variable quantization and entropy encoding to obtain a more or less lossy compression of raw image data. The computational complexity of both the encoding of raw data and decoding of the image from the compressed data is approximately equal.
Status: Available
- Looking for 1 Master student
- Supervision: David Bellasi
Character
- 10% Theory
- 60% Matlab Simulation
- 30% VLSI design
Prerequisites
- Matlab, VHDL
- VLSI I