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COMPUTATIONALLY EFFICIENT ALGORITHM FOR PARALLEL IMPLEMENTATION OF ZEROTREE CODING

COMPUTATIONALLY EFFICIENT ALGORITHM FOR PARALLEL IMPLEMENTATION OF ZEROTREE CODING. Saikat Mandal Yogesh Jashnani Prof. Yu Hen Hu ECE 734 Spring 2004. Motivation. Perform an in-depth analysis of Embedded Zerotree coding (EZW) Identify areas of optimization in the algorithm

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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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