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VLSI ARCHITECTURE FOR LOW POWER VARIABLE LENGTH ENCODING AND DECODING FOR IMAGE PROCESSING APPLICATIONS

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Author(s): Vijaya Prakash. A.M | K.S. Gurumurthy

Journal: International Journal of Advances in Engineering and Technology
ISSN 2231-1963

Volume: 1;
Issue: 1;
Start page: 105;
Date: 2012;
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Keywords: Variable Length Encoding (VLE) | Variable Length Decoding (VLD) | Joint Photographic Expert Group (JPEG) | Image Compression | Low Power Design | Very Large Scale Integration (VLSI).

ABSTRACT
The image data compression has been an active research area for image processing over the last decade [1] and has been used in a variety of applications. This paper investigates the implementation of Low Power VLSI architecture for image compression, which uses Variable Length Coding method to compress JPEG signals [1]. The architecture is proposed for the quantized DCT output [5]. The proposed architecture consists of three optimized blocks, viz, Zigzag scanning, Run-length coding and Huffman coding [17]. In the proposed architecture, Zigzag scanner uses two RAM memories in parallel to make the scanning faster. The Run-length coder in the architecture, counts the number of intermediate zeros in between the successive non-zero DCT coefficients unlike the traditional run-length coder which counts the repeating string of coefficients to compress data [20]. The complexity of the Huffman coder is reduced by making use of a lookup table formed by arranging the {run, value} combinations in the order of decreasing probabilities with associated variable length codes [14]. The VLSI architecture of the design is implemented [12] using Verilog HDL with Low Power approches . The proposed hardware architecture for image compression was synthesized using RTL complier and it was mapped using 90nm standard cells. The Simulation is done using Modelsim. The synthesis is done using RTL compiler from CADENCE. The back end design like Layout is done using IC Compiler. Power consumptions of variable length encoder and decoder are limited to 0.798mW and 0.884mW with minimum area. The Experimental results confirms that 53% power saving is achieved in the dynamic power of huffman decoding [6] by including the lookup table approach and also a 27% of power saving is achieved in the RL-Huffman encoder [8].
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