Quantization: Yao Wang's Video Processing and Communications
VQ: Yao Wang's Video Processing and Communications
Convolutional Codes: Shu Lin's Error Control Coding (2nd Edition)
Showing posts with label tech.. Show all posts
Showing posts with label tech.. Show all posts
Tuesday, July 21, 2009
Tuesday, July 14, 2009
Sunday, July 12, 2009
QUANTIZATION AND R-D FUNCTION
Data Compression: http://www.data-compression.com/theory.shtml
Quantization has two important properties: 1) a Distortion resulting from the approximation and 2) a Bit-Rate resulting from binary encoding of its levels. Therefore the Quantizer design problem is a Rate-Distortion optimization type. (http://en.wikipedia.org/wiki/Quantization_(signal_processing))
A good tutorial on Vector Quantization: http://www.data-compression.com/vq.html. In 1980, Linde, Buzo, and Gray (LBG) proposed a VQ design algorithm based on a training sequence.
Some VQers:
Rate-Distortion:
A good tutorial by Bernd Girod: http://www.stanford.edu/class/ee368b/Handouts/04-RateDistortionTheory.pdf
"Mutual Information" I(U;V) is the information that symbol U and symbol V convey about each other. Equivalently, I(U;V) is the communicated amount of information.
"Channel Capacity" C is the maximum mutual information between the transmitter and the receiver.
It is known that the Gaussian source is the most "difficult" source to encode: for a given mean square error, it requires the greatest number of bits. The performance of a practical compression system working on—say—images, may well be below the R(D) lower bound shown. ( http://en.wikipedia.org/wiki/Rate%E2%80%93distortion_theory )
Wyner and Ziv's paper "The Rate-Distortion Function for Source Coding with Side Information at the Decoder" provides the R-D function for the lossy DSC as well as its derivation. The proof procedure is crazy. I will read it when necessary.
Quantization has two important properties: 1) a Distortion resulting from the approximation and 2) a Bit-Rate resulting from binary encoding of its levels. Therefore the Quantizer design problem is a Rate-Distortion optimization type. (http://en.wikipedia.org/wiki/Quantization_(signal_processing))
A good tutorial on Vector Quantization: http://www.data-compression.com/vq.html. In 1980, Linde, Buzo, and Gray (LBG) proposed a VQ design algorithm based on a training sequence.
Some VQers:
- Stanley Ahalt
- Jim Fowler
- Allen Gersho
- Robert M. Gray
- Batuhan Ulug
Rate-Distortion:
A good tutorial by Bernd Girod: http://www.stanford.edu/class/ee368b/Handouts/04-RateDistortionTheory.pdf
"Mutual Information" I(U;V) is the information that symbol U and symbol V convey about each other. Equivalently, I(U;V) is the communicated amount of information.
"Channel Capacity" C is the maximum mutual information between the transmitter and the receiver.
It is known that the Gaussian source is the most "difficult" source to encode: for a given mean square error, it requires the greatest number of bits. The performance of a practical compression system working on—say—images, may well be below the R(D) lower bound shown. ( http://en.wikipedia.org/wiki/Rate%E2%80%93distortion_theory )
Wyner and Ziv's paper "The Rate-Distortion Function for Source Coding with Side Information at the Decoder" provides the R-D function for the lossy DSC as well as its derivation. The proof procedure is crazy. I will read it when necessary.
Friday, January 23, 2009
MY FEDORA 10
Work as root : su + exit
Emacs Installation: yum install emacs
Deal with rar formate: yum install unrar
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