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Revision as of 13:20, 6 June 2015

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 (e.g., smart watches or wireless low-power webcams).


In this project, we are interested in applying CS to low resolution image compression and compare it to well-established strategies. E.g., 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. The goal of this project is to see whether the assymmetry of CS can be leveraged to reduce the hardware complexity and power consumption of the encoding stage, and how the compression performance and image quality compare to JPEG.


What topics will you deal with:

- Image compression, in general, and JPEG, in particular.
- The basics of DCT (Discrete Cosine Transform) and FFT (Fast Fourier Transform)
- The basics of entropy encoding, Huffman encoding in particular
- The basics of Compressed Sensing

Status: Available

Looking for 1 Master student
Supervision: David Bellasi

Character

20% Theory
60% Matlab Simulation
20% VLSI design

Prerequisites

Matlab, VHDL
VLSI I

Professor

Luca Benini