Abstract
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 theXilinx core.
Highlights
Many applications such as video surveillance and patient monitoring systems require many cameras for effective tracking of living and nonliving objects
To manage the huge amount of data generated by several cameras, we proposed an optical implementation of an image compression based on discrete cosine transform (DCT) algorithm in [1]
In this paper we propose a digital realization of an optimized VLSI for image compression system
Summary
Many applications such as video surveillance and patient monitoring systems require many cameras for effective tracking of living and nonliving objects. To manage the huge amount of data generated by several cameras, we proposed an optical implementation of an image compression based on DCT algorithm in [1]. This solution suffers from bad image quality and higher material complexity. Since the DCT computation and quantization processes are computation intensive, several algorithms are proposed in literature for computing them efficiently in dedicated hardware Research in this domain can be classified into three parts.
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