We present a single-chip DVD processor. It combines two 32-b RISC cores, a 24-b/32-b audio DSP, video MPEG and subpicture decoders, a graphics and video processor with six video DACs that supports both progressive and interlaced output video, audio, video, and I/O interface units, and several dedicated processing units. In addition, the processor integrates the front-end channel decoder and error correction functions, traditionally part of the DVD loader circuitry. The DVD processor has been implemented using a standard-cell library in 0.18 /spl mu/m CMOS technology.

We present a single-chip, MPEG-2 main profile at main level, audio and video encoder and decoder. It combines a RISC core, a 24-bit DSP, video and audio interface units, and several dedicated processing units. A programmable video interface unit supports multiple modes of pre- and post-processing and on-screen display (OSD). The codec has been implemented using a standard-cell library in 0.18 /spl mu/m CMOS technology.

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.

In this paper we present a single chip MPEG-2, MP@ML, video encoder/decoder. It combines a RISC core and several dedicated processing units organized as a process pipeline. It includes a generic 16-bit host interface and direct interfaces to video modulators and demodulators. It has been implemented using a standard-cell library, in a four-layer metal, 0.35 /spl mu/m CMOS technology. Its small size (less than 3 million transistors) and low implementation cost make it suitable for a wide range of consumer applications.

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.

In this paper we present a JPEG-compliant method for the efficient variable quantization of compound documents. By measuring DCT activity in each of the 8/spl times/8 blocks, our scheme automatically detects the image parts and text parts of a document and adjusts the quantization scaling factors so that text blocks are compressed at higher quality than image blocks. Experimental results from three different quantization schemes are also reported.

This paper presents a technique for deblurring noisy images. It includes two processing blocks, one for denoising and another for blind image restoration. The denoising step is based on the theories of singular value decomposition and compression-based filtering. The deblurring step is based on a double-regularization technique. Experimental results show that the combination of these techniques is quite effective in restoring severely blurred and noise-corrupted images, without prior knowledge of either the noise or image characteristics.

The visual quality of scanned color documents suffers because of inherent limitations in color scanners. In cases where the scanned documents will also be compressed using the JPEG standard, we present a new technique that sharpens the compressed images in the DCT domain. Sharpening is achieved by suitably scaling each element of the encoding quantization table to enhance the high-frequency characteristics of the image. Experimental results show improved text and image quality with no additional computation cost.

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.

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.

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.

Encoding noisy data is a challenging problem in data compression. Recent results show that data compression and filtering are closely related. Exploiting these ideas, we propose a simple technique for vector quantization (VQ) coding of noisy images, based on pre-filtering only the training set prior to codebook design. We find that our technique results in significant improvement in image quality, comparable to that obtained by the computationally more expensive approach of pre-filtering both the training set and the images to be encoded. A preliminary justification of our results from the theory of Occam filtering is also provided.

An Occam filter employs lossy data compression to separate signal from noise. Previously it was shown that Occam filters are useful for filtering random noise from discrete samples of a deterministic and continuous signal. In this paper, we show that Occam filters can also be used to separate two stochastic sources, with the effectiveness of the separation depending on their relative compressibility. We then construct an Occam filter based on singular value decomposition (SVD) and apply it to digital images corrupted with Gaussian noise. We observe that the SVD-based Occam filter outperforms the wavelet-based denoising method of Donoho and Johnstone (1994) and DeVore and Lucier (1992).

Video capture devices, such as CCD cameras, are a significant source of noise in image sequences. Preprocessing of video sequences with spatial filtering techniques usually improves their compressibility. We present a block-based, non-linear filtering algorithm based on the theories of SVD and compression-based filtering. A novel noise estimation algorithm allows us to operate on the input data without any prior knowledge of either the noise or signal characteristics. Experiments with real video sequences and an MPEG codec have shown that SVD based filters preserve edge details and can significantly improve nearly-lossless compression ratios by 15%.

We present a novel non-linear filtering technique for random noise, based on the principle that random noise is hard to compress. In contrast to spectral filters, the proposed technique does not require a priori knowledge of the noise and signal characteristics. We demonstrate the technique by filtering additive random noise using a compression algorithm based on piecewise linear approximation. A single chip design of the filtering algorithm is also presented. It includes two multiplier-accumulator units, an adder, registers, and a short look-up table. The proposed implementation allows an output sample to be generated every four cycles on average.>

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.>

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.

The authors explore compression using an optimal algorithm for the approximation of waveforms with piecewise linear functions. They describe a modification of the algorithm that is provably good, but simple enough for the associated hardware implementation to be presentable.>

The authors present models and techniques for the task and I/O allocation and scheduling problem, subject to task precedence, memory requirements, and interprocessor communication costs. The models take into account the special characteristics of new architectures and can handle both sequential and parallel I/O and program execution within a processor. Both linear and nonlinear memory requirements can be modeled. By distinguishing between tasks that require all the output data from a predecessor before they begin execution and tasks that require only partial data, the models are more realistic and the accuracy and efficiency of the schedules is further improved. A simple branch-and-bound technique is presented, and it is applied to the solution of the task allocation problem. An example from the scheduling of a measurement application that includes filtering and FFT operations is also presented.>

A major problem in using SVD as a tool in determining the effective rank of a perturbed matrix, is that of distinguishing between significant small and insignificant large singular values. In this paper we derive confidence regions for the perturbed singular values of matrices with noisy observation data. The analysis is based on the perturbation theory of singular values and classical significance testing. The threshold bounds depend on the dimension of the matrix, the noise variance and a predefined statistical level of significance. The results are applied to the problem of determining the effective order of a linear system from the approximate rank of a sample autocorrelation matrix. Numerical examples are given.

We consider the evaluation of the order of a linear system transfer function represented as an AR or an ARMA model based on the use of the singular value decomposition technique for the efficient determination of the rank of a matrix. Inputs to the system are modeled as binary-valued random data and outputs of the system are observed in the presence of uncorrelated noises. Results are obtained for the case when the relevant statistics given in autocorrelation and crosscorrelation values are assumed to be available as well as the case when the required statistics are computed explicitly from the sequence sample values. Various numerical examples are considered and shown to be more efficient than the Woodside determinant ratio approach.