Balas K. Natarajan

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.

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.

Filtering random noise from deterministic signals via data compression

We present a novel technique for the design of filters for random noise, leading to a class of filters called Occam filters. The essence of the technique is that when a lossy data compression algorithm is applied to a noisy signal with the allowed loss set equal to the noise strength, the loss and the noise tend to cancel rather than add. We give two illustrative applications of the technique to univariate signals. We also prove asymptotic convergence bounds on the effectiveness of Occam filters.

A fast approximate algorithm for scaling down digital images in the DCT domain

We present a fast approximate algorithm for scaling down an image by a factor of two, when the input and output streams are both in the form of 8/spl times/8 discrete cosine transform (DCT) transform blocks, as with JPEG coded images. Roughly speaking, the algorithm requires 80% fewer operations than the naive algorithm of inverting the transform, scaling in the spatial domain, and transforming the resulting image.

Compiler synthesized dynamic branch prediction

Branch prediction is the predominant approach for minimizing the pipeline breaks caused by branch instructions. Traditionally, branch prediction is accomplished in one of two ways, static prediction at compile-time via compiler analysis or dynamic prediction at run-time via special hardware structures. In this paper, we propose a novel technique that aims to combine the strengths of the two approaches-the lower cost of compile-time analysis with the effectiveness of dynamic prediction. Specifically, we propose that the compiler use profile feedback to define a prediction function for each branch and insert a few explicit instructions per branch into the compiled code to compute the prediction function. These instructions are carefully selected to predict the direction of the branch using any information available during run-time. A strength of this approach is that information beyond branch history can be used to make predictions, such as the contents of the architectural registers. To substantiate our proposal, we present an algorithm for selecting the prediction instructions, and demonstrate the performance of the approach against contemporary static and dynamic branch prediction strategies.

Filtering random noise via data compression

A general technique is suggested for reducing random noise from signals using data compression in conjunction with the principle of Occams Razor. Not only are classical spectral filters realisable as a particular instance of the technique, but more powerful nonlinear filters fall within its scope.<<ETX>>

A formal framework for speedup learning from problems and solutions

Speedup learning seeks to improve the computational efficiency of problem solving with experience. In this paper, we develop a formal framework for learning efficient problem solving from random problems and their solutions. We apply this framework to two different representations of learned knowledge, namely control rules and macro-operators, and prove theorems that identify sufficient conditions for learning in each representation. Our proofs are constructive in that they are accompanied with learning algorithms. Our framework captures both empirical and explanation-based speedup learning in a unified fashion. We illustrate our framework with implementations in two domains: symbolic integration and Eight Puzzle. This work integrates many strands of experimental and theoretical work in machine learning, including empirical learning of control rules, macro-operator learning, Explanation-Based Learning (EBL), and Probably Approximately Correct (PAC) Learning.

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. >

Occam filters for stochastic sources with application to digital images

An Occam filter employs lossy data compression to separate signal from noise. Previously, it was shown that Occam filters can filter random noise from deterministic signals. Here, we show that Occam filters can also separate two stochastic sources, depending on their relative compressibility. We also compare the performance of Occam filters and wavelet-based denoising on digital images.

Low-complexity algorithm and architecture for block-based motion estimation via one-bit transforms

We present a strategy 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 are also presented.