Konstantinos Konstantinides

Low-complexity block-based motion estimation via one-bit transforms

We present an algorithm and a hardware architecture for block-based motion estimation that involves transforming video sequences from a multibit to a one-bit/pixel representation and then applying conventional motion estimation search strategies. This results in substantial reductions in arithmetic and hardware complexity and reduced power consumption, while maintaining good compression performance. Experimental results and a custom hardware design using a linear array of processing elements are also presented.

The Khoros software development environment for image and signal processing

Data flow visual language systems allow users to graphically create a block diagram of their applications and interactively control input, output, and system variables. Khoros is an integrated software development environment for information processing and visualization. It is particularly attractive for image processing because of its rich collection of tools for image and digital signal processing. This paper presents a general overview of Khoros with emphasis on its image processing and DSP tools. Various examples are presented and the future direction of Khoros is discussed.

Statistical analysis of effective singular values in matrix rank determination

A major problem in using SVD (singular-value decomposition) as a tool in determining the effective rank of a perturbed matrix is that of distinguishing between significantly small and significantly large singular values to the end, conference regions are derived for the perturbed singular values of matrices with noisy observation data. The analysis is based on the theories of perturbations of singular values and statistical significance test. Threshold bounds for perturbation due to finite-precision and i.i.d. random models are evaluated. In random models, the threshold bounds depend on the dimension of the matrix, the noisy variance, and predefined statistical level of significance. Results applied to the problem of determining the effective order of a linear autoregressive system from the approximate rank of a sample autocorrelation matrix are considered. Various numerical examples illustrating the usefulness of these bounds and comparisons to other previously known approaches are given. >

Noise estimation and filtering using block-based singular value decomposition

Preprocessing of image and video sequences with spatial filtering techniques usually improves the image quality and compressibility. We present a block-based, nonlinear filtering algorithm based on singular value decomposition and compression-based filtering. Experiments show that the proposed filter preserves edge details and can significantly improve the compression performance.

Fast subband filtering in MPEG audio coding

Subband filtering is one of the most compute-intensive operations in the MPEG audio coding standard. The author proves that the matrixing operations in MPEG subband filtering can be efficiently computed using fast 32-point DCT or inverse DCT (IDCT) algorithms.<<ETX>>

Task allocation and scheduling models for multiprocessor digital signal processing

Task allocation and scheduling models for distributed digital signal processing are presented. The notions of block-type and stream-type tasks in signal processing application are introduced, and models for sequential and parallel I/O are presented. By extending the traditional models, more accurate schedules can be obtained. Those models can be further enhanced by allowing additional restrictions on the number of parallel I/O ports and the amount of parallelism on memory access. The deterministic nature of digital signal processing algorithms allows for more computationally intensive and accurate task allocation techniques to be performed at compile time. By applying a branch and bound algorithm, the task allocation problem can easily be solved for a variety of scheduling models and various system restrictions. >

Image sharpening in the JPEG domain

We present a new technique for sharpening compressed images in the discrete-cosine-transform domain. For images compressed using the JPEG standard, image sharpening is achieved by suitably scaling each element of the encoding quantization table to enhance the high-frequency characteristics of the image. The modified version of the encoding table is then transmitted in lieu of the original. Experimental results with scanned images show improved text and image quality with no additional computation cost and without affecting compressibility.

A JPEG variable quantization method for compound documents

In this paper, we present a JPEC-compliant method for the efficient compression of compound documents using variable quantization. Based on the DCT activity of each 8 x 8 block, our scheme automatically adjusts the quantization scaling factors so that test blocks are compressed at higher quality than image blocks. Results from three different quantization mappings are also reported.

An architecture for lossy compression of waveforms using piecewise-linear approximation

Lossy compression schemes are often desirable in many signal processing applications such as the compression of ECG data. This paper presents a relaxation of a provably good algorithm for lossy signal compression, based on the piecewise linear approximation of functions. The algorithm approximates the data within a given tolerance using a piecewise linear function. The paper also describes an architecture suitable for the single-chip implementation of the proposed algorithm. The design consists of control, two multiply/divide units, four adder/subtracter units, and an I/O interface unit. For uniformly sampled data, no division is required, and all operations can be completed in a pipelined manner in at most three cycles per sample point. The corresponding simplified architecture is also presented. >

Monolithic architectures for image processing and compression

Programmable IC architectures for image processing applications are reviewed. Chip sets that can be used for image and video compression and for traditional image processing systems like computer-vision systems, chip designs that can handle all three of the major multimedia compression, decompression, and transmission standards, ICs for generic image processing, and uniprocessor and multiprocessor image processing chips are discussed. Requirements for graphics architectures and image processing are outlined. >