A content-addressable memory architecture for image coding using vector quantization
- 1 January 1991
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Signal Processing
- Vol. 39 (9) , 2066-2078
- https://doi.org/10.1109/78.134438
Abstract
An architecture suitable for real-time image coding using adaptive vector quantization (VQ) is presented. This architecture is based on the concept of content-addressable memory (CAM), where the data is accessed simultaneously and in parallel on the basis of its content. VQ essentially involves, for each input vector, a search operation to obtain the best match codeword. A speedup results if a CAM-based implementation is used. This speedup, coupled with the gains in execution time for the basic distortion operation, implies that even codebook generation is possible in real time (<32 ms). In using the CAM, the conventional mean square error measure is replaced by the absolute difference measure. This measure results in little degradation and in fact limits large errors. The regular and iterable architecture is particularly well suited for VLSI implementationKeywords
This publication has 27 references indexed in Scilit:
- Bit serial systolic chip set for real-time image codingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Vector quantizer architectures for speech and image codingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Real-time speech compression with a VLSI vector quantization processorPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Adaptive vector quantization by progressive codevector replacementPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Real-time VQ codebook generation hardware for speech processingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Design, selection and implementation of a content-addressable memory for a VLSI CMOS chip architectureIEE Proceedings E Computers and Digital Techniques, 1988
- Vector quantization of images based upon the Kohonen self-organizing feature mapsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1988
- Image Compression Based On Vector Quantization With Finite MemoryOptical Engineering, 1987
- Classified Vector Quantization of ImagesIEEE Transactions on Communications, 1986
- Vector quantiser of video signalsElectronics Letters, 1982