Hardware implementation of daubechies wavelet transforms using folded AIQ mapping

The Discrete Wavelet Transform (DWT) is a popular tool in the field of image and video compression applications. Because of its multi-resolution representation capability, the DWT has been used effectively in applications such as transient signal analysis, computer vision, texture analysis, cell detection, and image compression. Daubechies wavelets are one of the popular transforms in the wavelet family. Daubechies filters provide excellent spatial and spectral locality-properties which make them useful in image compression. In this thesis, we present an efficient implementation of a shared hardware core to compute two 8-point Daubechies wavelet transforms. The architecture is based on a new two-level folded mapping technique, an improved version of the Algebraic Integer Quantization (AIQ). The scheme is developed on the factorization and decomposition of the transform coefficients that exploits the symmetrical and wrapping structure of the matrices. The proposed architecture is parallel, pipelined, and multiplexed. Compared to existing designs, the proposed scheme reduces significantly the hardware cost, critical path delay and power consumption with a higher throughput rate. Later, we have briefly presented a new mapping scheme to error-freely compute the Daubechies-8 tap wavelet transform, which is the next transform of Daubechies-6 in the Daubechies wavelet series. The multidimensional technique maps the irrational transformation basis coefficients with integers and results in considerable reduction in hardware and power consumption, and significant improvement in image reconstruction quality

Exploiting parallelism within multidimensional multirate digital signal processing systems

The intense requirements for high processing rates of multidimensional Digital Signal Processing systems in practical applications justify the Application Specific Integrated Circuits designs and parallel processing implementations. In this dissertation, we propose novel theories, methodologies and architectures in designing high-performance VLSI implementations for general multidimensional multirate Digital Signal Processing systems by exploiting the parallelism within those applications. To systematically exploit the parallelism within the multidimensional multirate DSP algorithms, we develop novel transformations including (1) nonlinear I/O data space transforms, (2) intercalation transforms, and (3) multidimensional multirate unfolding transforms. These transformations are applied to the algorithms leading to systematic methodologies in high-performance architectural designs. With the novel design methodologies, we develop several architectures with parallel and distributed processing features for implementing multidimensional multirate applications. Experimental results have shown that those architectures are much more efficient in terms of execution time and/or hardware cost compared with existing hardware implementations

A Novel Methodology for Memory Reduction in Distributed Arithmetic Based Discrete Wavelet Transform

AbstractDiscrete Wavelet Transform (DWT) is widely used in image compression standards such as JPEG 2000. DWT can be implemented on FPGA using parallel Distributed Arithmetic (DA) architecture, which is suitable for low power implementation. However, the size of the memory in DA increases with the number of wavelet coefficients. In this paper, we propose a novel methodology to reduce the size of the Look-Up Tables (LUTs) used in DA for DWT. The table entries are sorted using Burrows-Wheeler Transform (BWT) and then compressed. The compressed table is stored in memory. During DWT/IDWT computation, without reconstructing the entire table we can recover only the required table entry. A comparative study of this methodology among different wavelets is performed. We demonstrate that the method is very effective for reducing the memory of DA architectures. A compression ratio of around 2.3:1 is achieved for the look-up table which stores the inner product of high-pass filter coefficients of Daubechies-4 (Db4) wavelet which is used in JPEG2000