DCT Implementation with Distributed Arithmetic
IEEE Transactions on Computers
A Low Power High Performance Distributed DCT Architecture
ISVLSI '02 Proceedings of the IEEE Computer Society Annual Symposium on VLSI
IEEE Transactions on Image Processing
IEEE Transactions on Circuits and Systems for Video Technology
A scaled DCT architecture with the CORDIC algorithm
IEEE Transactions on Signal Processing
Efficient VLSI architecture for video transcoding
IEEE Transactions on Consumer Electronics
A new time distributed DCT architecture for MPEG-4 hardware reference model
IEEE Transactions on Circuits and Systems for Video Technology
Systolic Designs for DCT Using a Low-Complexity Concurrent Convolutional Formulation
IEEE Transactions on Circuits and Systems for Video Technology
A 100 MHz 2-D 8×8 DCT/IDCT processor for HDTV applications
IEEE Transactions on Circuits and Systems for Video Technology
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The canonical signed digit (CSD) representation of constant coefficients is a unique signed data representation containing the fewest number of nonzero bits. Consequently, for constant multipliers, the number of additions and subtractions is minimized by CSD representation of constant coefficients. This technique is mainly used for finite impulse response (FIR) filter by reducing the number of partial products. In this paper, we use CSD with a novel common subexpression elimination (CSE) scheme on the optimal Loeffler algorithm for the computation of discrete cosine transform (DCT). To meet the challenges of low-power and high-speed processing, we present an optimized image compression scheme based on two-dimensional DCT. Finally, a novel and a simple reconfigurable quantization method combined with DCT computation is presented to effectively save the computational complexity. We present here a new DCT architecture based on the proposed technique. From the experimental results obtained from the FPGA prototype we find that the proposed design has several advantages in terms of power reduction, speed performance, and saving of silicon area along with PSNR improvement over the existing designs as well as the Xilinx core.