Difference between revisions of "Compressed Sensing vs JPEG"
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==Short Description== | ==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 | + | 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 asymmetric CODEC making its application in low-power 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 | + | 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 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. |
− | + | This project may be extended to include the design of an ASIC. | |
+ | |||
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+ | These are the topics you will deal with: | ||
:- Image compression, in general, and JPEG, in particular. | :- Image compression, in general, and JPEG, in particular. | ||
:- The basics of DCT (Discrete Cosine Transform) and FFT (Fast Fourier Transform) | :- The basics of DCT (Discrete Cosine Transform) and FFT (Fast Fourier Transform) |
Revision as of 14:23, 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 asymmetric CODEC making its application in low-power 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 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.
This project may be extended to include the design of an ASIC.
These are the topics you will 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