Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
461765 | Microprocessors and Microsystems | 2008 | 12 Pages |
Abstract
This report describes the design of a modular, massive-parallel, neural-network (NN)-based vector quantizer for real-time video coding. The NN is a self-organizing map (SOM) that works only in the training phase for codebook generation, only at the recall phase for real-time image coding, or in both phases for adaptive applications. The neural net can be learned using batch or adaptive training and is controlled by an inside circuit, finite-state machine-based hard controller. The SOM is described in VHDL and implemented on electrically (FPGA) and mask (standard-cell) programmable devices.
Keywords
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Computer Networks and Communications
Authors
Agustin Ramirez-Agundis, Rafael Gadea-Girones, Ricardo Colom-Palero,