TY - GEN
T1 - Applications of gain-spectral-block classification in image coding
AU - Jafarkhani, Hamid
AU - Kerry, M.
AU - Farvardin, Nariman
PY - 1995
Y1 - 1995
N2 - This work focuses on the development of a two-level image block classification scheme and its application to low bit rate image coding. Using this classifier, we present two adaptive encoding structures, one based on vector quantization (VQ) and the other based on transform coding. The first stage of our system classifies the image blocks into K 1 classes based on the block grain, similar to the well-known classification scheme of Chen and Smith, but allows for the possibility of a variable number of vectors per class. To do this, we develop an iterative mini-max algorithm that adjusts the vectors among the classes so that the resulting mean-normalized standard deviation of the gain values within any class is similar to all other classes. After classifying based on block gain values, we further classify each gain-class into K 2 spectral classes. This is accomplished by performing a 1D LPC-type analysis of each block, and clustering the resulting LPC vectors using a vector quantizer (VQ) with K 2 codevectors. In order to make this spectral matching meaningful, the VQ is designed and implemented using the Itakura-Saito distortion measure. The resulting two-level classification scheme thus classifies an image into K = K 1K 2 classes. A system consisting of a bank of K fixed-rate Multi-Stage VQ's and a DCT based system are then used to examine the usefulness of the proposed approaches for classification.
AB - This work focuses on the development of a two-level image block classification scheme and its application to low bit rate image coding. Using this classifier, we present two adaptive encoding structures, one based on vector quantization (VQ) and the other based on transform coding. The first stage of our system classifies the image blocks into K 1 classes based on the block grain, similar to the well-known classification scheme of Chen and Smith, but allows for the possibility of a variable number of vectors per class. To do this, we develop an iterative mini-max algorithm that adjusts the vectors among the classes so that the resulting mean-normalized standard deviation of the gain values within any class is similar to all other classes. After classifying based on block gain values, we further classify each gain-class into K 2 spectral classes. This is accomplished by performing a 1D LPC-type analysis of each block, and clustering the resulting LPC vectors using a vector quantizer (VQ) with K 2 codevectors. In order to make this spectral matching meaningful, the VQ is designed and implemented using the Itakura-Saito distortion measure. The resulting two-level classification scheme thus classifies an image into K = K 1K 2 classes. A system consisting of a bank of K fixed-rate Multi-Stage VQ's and a DCT based system are then used to examine the usefulness of the proposed approaches for classification.
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M3 - Conference contribution
AN - SCOPUS:0029458240
SN - 0819417653
SN - 9780819417657
T3 - Proceedings of SPIE - The International Society for Optical Engineering
SP - 88
EP - 99
BT - Proceedings of SPIE - The International Society for Optical Engineering
A2 - Rabbani, Majid
A2 - Delp, Edward J.
A2 - Rajala, Sarah A.
T2 - Still-Image Compression
Y2 - 7 February 1995 through 8 February 1995
ER -