Investigation of a Novel Common Subexpression Elimination Method for Low Power and Area Efficient DCT Architecture

Abstract

A wide interest has been observed to find a low power and area efficient hardware design of discrete cosine transform (DCT) algorithm. This research work proposed a novel Common Subexpression Elimination (CSE) based pipelined architecture for DCT, aimed at reproducing the cost metrics of power and area while maintaining high speed and accuracy in DCT applications. The proposed design combines the techniques of Canonical Signed Digit (CSD) representation and CSE to implement the multiplier-less method for fixed constant multiplication of DCT coefficients. Furthermore, symmetry in the DCT coefficient matrix is used with CSE to further decrease the number of arithmetic operations. This architecture needs a single-port memory to feed the inputs instead of multiport memory, which leads to reduction of the hardware cost and area. From the analysis of experimental results and performance comparisons, it is observed that the proposed scheme uses minimum logic utilizing mere 340 slices and 22 adders. Moreover, this design meets the real time constraints of different video/image coders and peak-signal-to-noise-ratio (PSNR) requirements. Furthermore, the proposed technique has significant advantages over recent well-known methods along with accuracy in terms of power reduction, silicon area usage, and maximum operating frequency by 41%, 15%, and 15%, respectively.

A Multiscale Denoising Framework Using Detection Theory with Application to Images from CMOS/CCD Sensors

“…The second-pipelined stage computes the transform (UWT/DTCWT) operation on the pre-processed signal of the first stage. Real-time implementations of different transforms are reported in the literature [54,55,56,57]. Addition and average mathematical operations are needed to be executed in the third-pipelined stage of the proposed algorithm.…”

“…In order to obtain the denoised image, the thresholded multiscale coefficients from Equation (25) are reconstructed by employing the transform T−1(·) as follows trueS^=T−1(trueu^k(i)), where sfalse^ an estimate of the true signal (or image) s, or simply stated the denoised signal or image. Implementation of various forward and inverse wavelet transforms is reported in [54,55,56,57].…”