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COMPUTATIONALLY EFFICIENT ALGORITHM FOR PARALLEL IMPLEMENTATION OF ZEROTREE CODING

This paper presents a computationally efficient algorithm for the parallel implementation of Embedded Zerotree Coding (EZW) using an integer-based lifted wavelet transform. By optimizing key areas of the EZW algorithm and integrating lifting techniques, we achieve significant reductions in complexity and memory requirements, eliminating the need for floating-point units. The proposed methods include in-place processing, parallel zerotree identification, and efficient memory management, resulting in enhanced runtime performance. Future work aims to further explore pipelining and optimal parallel stages for diverse input sizes.

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COMPUTATIONALLY EFFICIENT ALGORITHM FOR PARALLEL IMPLEMENTATION OF ZEROTREE CODING

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  1. COMPUTATIONALLY EFFICIENT ALGORITHM FOR PARALLEL IMPLEMENTATION OF ZEROTREE CODING Saikat Mandal Yogesh Jashnani Prof. Yu Hen Hu ECE 734 Spring 2004

  2. Motivation • Perform an in-depth analysis of Embedded Zerotree coding (EZW) • Identify areas of optimization in the algorithm • Apply concepts learned in class for efficient hardware implementation of EZW • Incorporate integer based lifted wavelet algorithm to reduce complexity

  3. Approach 1:DWT • Use of lifting leads to speed-up compared to FWT, reduces MAC operations • In-place Implementation • Introduces parallelism within the wavelet computation, and with EZW • Integer based approach • Reduces memory requirements (float to int) • No need of floating point units on chip

  4. Psedo Code of Integer based transform Forward Inverse For i = M : 1 For i = 1: M end end

  5. Approach 2:Embedded Zerotree Coding (EZW) • Incorporate a fast technique to identify zerotrees prior to encoding. • Simple Bit-wise ORing operation to determine the elements of zerotree. • Scale-1 zerotree coefficients are discarded after first step, saving 3/4th memory required to store the zerotree. • Initialize a zerotree map whose elements are determined in parallel with wavelet transform operation.

  6. Lifting – EZW interface • Literature focuses on EZW or lifting, not on combination of the two • Lifting and Zerotree identification can be done in parallel • 3 more lifting steps are needed for the scaling coefficient in integer based transform • scaling SKIPPED in most algorithms, but VITAL for EZW

  7. ZEROTREE DWT

  8. RESULTS (1) • Entire algorithm was implemented in ANSI C • PSNR • Memory(512x512 1byte/pixel) SPIHT : 1.125 MB

  9. RESULTS (2) • Computational costs[1] • Runtime speed (in ms)

  10. FUTURE WORK • Further investigate the parallel implementation of lifting and EZW • Find optimum solution for number of parallel stages and level of pipelining such that HUE is maximum for a wide range of input image sizes and levels of decomposition • Listless Encoding

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