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Compressed Sensing vs JPEG

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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

Professor

Luca Benini