The Experts below are selected from a list of 48 Experts worldwide ranked by ideXlab platform

Dong-joon Choi - One of the best experts on this subject based on the ideXlab platform.

  • Quantization Error According to Bit Truncation Method in 4k-FFT Algorithm
    Lecture Notes in Electrical Engineering, 2013
    Co-Authors: Sang-jung Ra, Dong-joon Choi, Sangjin Ryoo
    Abstract:

    In this paper, we compare a Quantization Error performance of FFT algorithm according to bit truncation method. 4k-FFT algorithm of OFDM is proposed and implemented in field programmable gate arrays (FPGAs). We analyze the Quantization Error performance according to bit truncation method. Measured results show the Maximum Quantization Error of 6.042152/6.067595 (real/imaginary value in 12 stage MSB truncation), 3.112953/2.627594 (real/imaginary value in 12 stage LSB truncation), 0.006065/0.005448 (real/imaginary value in 6 stage LSB/6 stage MSB truncation) in 1st method. And measured results show the Maximum Quantization Error of 0.001464/0.00129 (real/imaginary value in truncation after FFT) in 2nd method.

  • ICTC - Simulation results according to bit truncation in 4k-FFT algorithm
    2012 International Conference on ICT Convergence (ICTC), 2012
    Co-Authors: Sang-jung Ra, Dong-joon Choi
    Abstract:

    In this paper, we present an efficient design and implementation of FFT algorithm which is an important part of OFDM system. 4k-FFT algorithm is proposed and implemented in field programmable gate arrays (FPGAs). We measure the Quantization Error of FFT algorithm. Measured results show the Maximum Quantization Error of 6.793259/5.662575 (real/imaginary value in 12 stage MSB truncation), 3.112945/2.627586 (real/imaginary value in 12 stage LSB truncation), 0.000756/0.000458 (real/imaginary value in 6 stage LSB/6 stage MSB truncation).

  • Simulation results according to bit truncation in 4k-FFT algorithm
    2012 International Conference on ICT Convergence (ICTC), 2012
    Co-Authors: Sang-jung Ra, Dong-joon Choi
    Abstract:

    In this paper, we present an efficient design and implementation of FFT algorithm which is an important part of OFDM system. 4k-FFT algorithm is proposed and implemented in field programmable gate arrays (FPGAs). We measure the Quantization Error of FFT algorithm. Measured results show the Maximum Quantization Error of 6.793259/5.662575 (real/imaginary value in 12 stage MSB truncation), 3.112945/2.627586 (real/imaginary value in 12 stage LSB truncation), 0.000756/0.000458 (real/imaginary value in 6 stage LSB/6 stage MSB truncation).

D.c. Coll - One of the best experts on this subject based on the ideXlab platform.

  • Multilevel block truncation coding using a minimax Error criterion for high-fidelity compression of digital images
    IEEE Transactions on Communications, 1993
    Co-Authors: Y. Wu, D.c. Coll
    Abstract:

    An encoding technique called multilevel block truncation coding that preserves the spatial details in digital images while achieving a reasonable compression ratio is described. An adaptive quantizer-level allocation scheme which minimizes the Maximum Quantization Error in each block and substantially reduces the computational complexity in the allocation of optimal Quantization levels is introduced. A 3.2:1 compression can be achieved by the multilevel block truncation coding itself. The truncated, or requantized, data are further compressed in a second pass using combined predictive coding, entropy coding, and vector Quantization. The second pass compression can be lossless or lossy. The total compression ratios are about 4.1:1 for lossless second-pass compression, and 6.2:1 for lossy second-pass compression. The subjective results of the coding algorithm are quite satisfactory, with no perceived visual degradation.

Sang-jung Ra - One of the best experts on this subject based on the ideXlab platform.

  • Quantization Error According to Bit Truncation Method in 4k-FFT Algorithm
    Lecture Notes in Electrical Engineering, 2013
    Co-Authors: Sang-jung Ra, Dong-joon Choi, Sangjin Ryoo
    Abstract:

    In this paper, we compare a Quantization Error performance of FFT algorithm according to bit truncation method. 4k-FFT algorithm of OFDM is proposed and implemented in field programmable gate arrays (FPGAs). We analyze the Quantization Error performance according to bit truncation method. Measured results show the Maximum Quantization Error of 6.042152/6.067595 (real/imaginary value in 12 stage MSB truncation), 3.112953/2.627594 (real/imaginary value in 12 stage LSB truncation), 0.006065/0.005448 (real/imaginary value in 6 stage LSB/6 stage MSB truncation) in 1st method. And measured results show the Maximum Quantization Error of 0.001464/0.00129 (real/imaginary value in truncation after FFT) in 2nd method.

  • ICTC - Simulation results according to bit truncation in 4k-FFT algorithm
    2012 International Conference on ICT Convergence (ICTC), 2012
    Co-Authors: Sang-jung Ra, Dong-joon Choi
    Abstract:

    In this paper, we present an efficient design and implementation of FFT algorithm which is an important part of OFDM system. 4k-FFT algorithm is proposed and implemented in field programmable gate arrays (FPGAs). We measure the Quantization Error of FFT algorithm. Measured results show the Maximum Quantization Error of 6.793259/5.662575 (real/imaginary value in 12 stage MSB truncation), 3.112945/2.627586 (real/imaginary value in 12 stage LSB truncation), 0.000756/0.000458 (real/imaginary value in 6 stage LSB/6 stage MSB truncation).

  • Simulation results according to bit truncation in 4k-FFT algorithm
    2012 International Conference on ICT Convergence (ICTC), 2012
    Co-Authors: Sang-jung Ra, Dong-joon Choi
    Abstract:

    In this paper, we present an efficient design and implementation of FFT algorithm which is an important part of OFDM system. 4k-FFT algorithm is proposed and implemented in field programmable gate arrays (FPGAs). We measure the Quantization Error of FFT algorithm. Measured results show the Maximum Quantization Error of 6.793259/5.662575 (real/imaginary value in 12 stage MSB truncation), 3.112945/2.627586 (real/imaginary value in 12 stage LSB truncation), 0.000756/0.000458 (real/imaginary value in 6 stage LSB/6 stage MSB truncation).

T.-c. Chen - One of the best experts on this subject based on the ideXlab platform.

  • A lattice vector Quantization using a geometric decomposition
    IEEE Transactions on Communications, 1990
    Co-Authors: T.-c. Chen
    Abstract:

    An efficient lattice vector Quantization design and the associated fast coding algorithm are proposed for high-bit-rate, high-quality data compression applications. The codewords are uniformly distributed and densely packed as 2/sup n/-dimensional lattice points, based on a geometric lattice decomposition technique. The Maximum Quantization Error has been chosen as the design criterion. For high-rate applications, it has the following advantages: (1) simple vector codeword generation; (2) no codewords need to be stored and only predetermined rules are used at encoder and decoder ends; (3) highly regular code structure, so that encoding is done via an inverse tree-search suitable for fast parallel processing, and decoding is done similar to a scalar quantizer; (4) high coding quality capability, viz. the Maximum Quantization distortion can be prespecified to a desired value and the entire hyper-region is covered uniformly; and (5) dimensionality saving can be easily predicted and it can be achieved using fixed-length codes.

Y. Wu - One of the best experts on this subject based on the ideXlab platform.

  • Multilevel block truncation coding using a minimax Error criterion for high-fidelity compression of digital images
    IEEE Transactions on Communications, 1993
    Co-Authors: Y. Wu, D.c. Coll
    Abstract:

    An encoding technique called multilevel block truncation coding that preserves the spatial details in digital images while achieving a reasonable compression ratio is described. An adaptive quantizer-level allocation scheme which minimizes the Maximum Quantization Error in each block and substantially reduces the computational complexity in the allocation of optimal Quantization levels is introduced. A 3.2:1 compression can be achieved by the multilevel block truncation coding itself. The truncated, or requantized, data are further compressed in a second pass using combined predictive coding, entropy coding, and vector Quantization. The second pass compression can be lossless or lossy. The total compression ratios are about 4.1:1 for lossless second-pass compression, and 6.2:1 for lossy second-pass compression. The subjective results of the coding algorithm are quite satisfactory, with no perceived visual degradation.