We describe methods for the classification of brain state using quantitative analysis of the EEG (QEEG). Neurometric analysis of EEG collected from the 19 standard locations of the International 10-20 System already provides such a tool. In this work we demonstrate the effectiveness of this approach when the available inputs are reduced to a set of five frontal electrodes. This system has applications in certain critical clinical care situations, such as emergency room triage, when a full EEG might be unavailable, inconvenient, or time-consuming. Additionally, we augment the standard neurometric QEEG analysis with local discriminant basis features of the power spectrum and microstate-like features which exploit the rich temporal structure of the EEG. These enhancements provide clear gains in sensitivity and specificity on a representative database.

We propose a new method for the extraction of auditory brainstem responses (ABRs) from an EEG signal. It is based on adaptive filtering of signals in the wavelet domain, where the transform used is a nearly shift-invariant complex wavelet transform (CWT). We compare our algorithm to two existing methods. The first simply consists of bandpass filtering the input EEG signal followed by linear averaging. The second method uses signal-adaptive filtering in the Fourier domain based on phase variance computed at each spectral component of the FFT. Realistic models of EEG and ABR are generated for this comparison. Results show that the wavelet-based method consistently outperforms the other two methods for ABR signals with an initial signal-to-noise ratio less than -20 dB.

Scalability has become an important feature for video coding algorithm design for heterogeneous networks due to variable bandwidth, variable wired and wireless network conditions and variable terminal capabilities. However, the best video compression algorithms are based on temporal prediction through motion compensation which do not lend themselves naturally to a scalable framework. Hybrid coding schemes are being introduced that combine a base-layer motion-compensation (MC) coder with an enhancement layer which offers fine-grain scalability through an embedded or progressive coder. Such a framework usually results in some compression efficiency loss over the best single layer motion compensated scheme Here, we introduce a hybrid coding scheme which combines a base-layer MC coder and a finely scalable enhancement layer where the information from the base-layer is used to determine the location of the high energy signal in the enhancement layer. This coder provides better compression results than embedded approaches which do not rely on base layer information.

Traditional wavelet packet (WP) optimization techniques neglect information about the structure of the lossy part of the compression scheme. Such information, however, can help guide the optimization procedure so as to result in efficient WP structures. We propose a wavelet packet algorithm with a constrained rate-distortion optimization which makes it suited to subsequent tree-structured coding such as with the set partitioning in hierarchical trees (SPMT) algorithm. The (octave-band) wavelet transform lends itself to simple and coherent tree-shaped spatial relations which can then be used to define zero-trees. Yet, input images have different frequency distributions and an adaptive transform such as WP is bound to be more efficient on an image-by-image basis. With WP algorithms, the coefficients in the WP domain can be rearranged to produce what resembles (or simulates) the normal wavelet transform structure. This stage is usually performed to simplify the coding stage. However, an unconstrained optimization can result in a transformed image with complicated or incoherent tree-shaped spatial relations. This work aims to show that the efficiency of embedded coders such as SPMT and Shapiro's Zerotrees strongly depends on WP structures with coherent spatial tree relationships.

Article Free Access Share on A real-time scalable software video codec for collaborative applications over packet networks Authors: J. Hartung Multimedia Communications Research Laboratory, Lucent Technologies, Bell Laboratories 600 Mountain Avenue Murray Hill, NJ Multimedia Communications Research Laboratory, Lucent Technologies, Bell Laboratories 600 Mountain Avenue Murray Hill, NJView Profile , A. Jacquin Multimedia Communications Research Laboratory, Lucent Technologies, Bell Laboratories 600 Mountain Avenue Murray Hill, NJ Multimedia Communications Research Laboratory, Lucent Technologies, Bell Laboratories 600 Mountain Avenue Murray Hill, NJView Profile , J. Pawlyk Multimedia Communications Research Laboratory, Lucent Technologies, Bell Laboratories 600 Mountain Avenue Murray Hill, NJ Multimedia Communications Research Laboratory, Lucent Technologies, Bell Laboratories 600 Mountain Avenue Murray Hill, NJView Profile , K. L. Shipley Multimedia Communications Research Laboratory, Lucent Technologies, Bell Laboratories 600 Mountain Avenue Murray Hill, NJ Multimedia Communications Research Laboratory, Lucent Technologies, Bell Laboratories 600 Mountain Avenue Murray Hill, NJView Profile Authors Info & Claims MULTIMEDIA '98: Proceedings of the sixth ACM international conference on MultimediaSeptember 1998 Pages 419–426https://doi.org/10.1145/290747.290814Published:01 September 1998Publication History 8citation352DownloadsMetricsTotal Citations8Total Downloads352Last 12 Months3Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

This paper describes a prototype video coding platform meant for the conception and testing of multimedia products such as next-generation videophones. The platform is largely based on ITU-T Recommendation H.263, with a number of additional object-oriented quality enhancement features which make it especially well suited for very low bit-rate coding of "head-and-shoulders" video material typical of real-time multimedia applications, video teleconferencing, and video telephony. These features consist of: (1) segmentation into objects of interest, (2) segmentation-based prefiltering, (3) model-assisted rate control, (4) adaptive vector quantization, and finally (5) segmentation-based postfiltering. In the spirit of Recommendation H.263, these enhancements are modular and can be selectively turned on or off, thereby enabling a wide variety of coding modes.

We propose a postfiltering algorithm which adapts to global image quality as well as (optionally) to semantic image content extracted from the video sequence. This approach is in contrast to traditional postfiltering techniques which attempt to remove coding artifacts based on local signal characteristics only. Our postfilter is ideally suited to head-and-shoulders video coded at very low bit rates (less than 25.6 kbps), where coding artifacts are fairly strong and difficult to distinguish from fine image detail. Results are shown comparing head-and-shoulder sequences encoded at 16 kbps with an H.263-based codec to images postfiltered using the content-adaptive postfilter proposed. The postfilter manages to remove most of the mosquito artifacts introduced by the low-bit-rate coder while preserving a good rendition of facial detail.

This paper proposes a new normalized parametric rate-distortion model which can be efficiently applied to H.263-compatible video codecs and other codecs of similar type. The model is very efficient in terms of computational complexity and amount of required memory since it approximates all 31 rate-distortion relations (one for each quantization parameter QP) with only one curve by normalizing the energy of signals and the quantization distortion with functions of QP. We show how the model parameters can be obtained from experimental data. We also propose a quantizer selection scheme which uses the model to estimate the number of bits required for encoding 8/spl times/8 residual blocks. Experiments show that the model provides very good approximations of the rate and the distortion characteristics for all QPs, and that the proposed quantizer selection scheme works better than the scheme used in the MPEG-4 Video Verification Model Version 5.1.

We describe a method of object-selective quantizer control in a standard coding system based on MC DCT-CCITT Recommendation H.261. The approach is based on two novel algorithms, namely buffer rate modulation and buffer size modulation. By forcing the rate control algorithm to transfer a relatively small fraction-about 10-15% on average-of the total available bit rate from the coding of the non-facial to that of the facial area, in head-and-shoulders videoteleconferencing sequences, images with better-rendered facial features are obtained, i.e. blocky artifacts in the facial area are less pronounced and eye contact and lip-sync are preserved. The improvement was found to be perceptually significant on video sequences coded at the rate of 64 kbps, with 48 kbps allocated for the input (color) video signal in QCIF format.

We present a novel and practical way to integrate techniques from computer vision to low bit-rate coding systems for video teleconferencing applications. Our focus is to locate and track the faces of persons in typical head-and-shoulders video sequences, and to exploit the face location information in a ‘classical’ video coding/decoding system. The motivation is to enable the system to selectively encode various image areas and to produce psychologically pleasing coded images where faces are sharper. We refer to this approach as model-assisted coding. We propose a totally automatic, low-complexity algorithm, which robustly performs face detection and tracking. A priori assumptions regarding sequence content are minimal and the algorithm operates accurately even in cases of partial occlusion by moving objects. Face location information is exploited by a low bit-rate 3D subband-based video coder which uses both a novel model-assisted pixel-based motion compensation scheme, as well as model-assisted dynamic bit allocation with object-selective quantization. By transferring a small fraction of the total available bit-rate from the non-facial to the facial area, the coder produces images with better-rendered facial features. The improvement was found to be perceptually significant on video sequences coded at 96 kbps for an input luminance signal in CIF format. The technique is applicable to any video coding scheme that allows for fine-grain quantizer selection (e.g. MPEG, H.261), and can maintain full decoder compatibility.

We present a novel and practical way to integrate techniques from computer vision to low bit-rate coding systems for video teleconferencing applications. Our focus is to locate and track the faces and selected facial features of persons in typical head-and-shoulders video sequences, and to exploit the location information in a ‘classical’ video coding/decoding system. The motivation is to enable the system to encode selectively various image areas and to produce perceptually pleasing coded images where faces are sharper. We refer to this approach—a mix of classical waveform coding and model—based coding-as model-assisted coding. We propose two totally automatic algorithms which, respectively, perform the detection of a head outline, and identify an ‘eyes-nose-mouth’ region, both from downsampled binary thresholded edge images. The algorithms operate accurately and robustly, even in cases of significant head rotation or partial occlusion by moving objects. We show how the information about face and facial feature location can be advantageously exploited by low bit-rate waveform-based video coders. In particular, we describe a method of object-selective quantizer control in a standard coding system based on motion-compensated discrete cosine transform—CCITT's recommendation H.261. The approach is based on two novel algorithms, namely buffer rate modulation and buffer size modulation. By forcing the rate control algorithm to transfer a fraction of the total available bit-rate from the coding of the non-facial to that of the facial area, the coder produces images with better-rendered facial features, i.e. coding artefacts in the facial area are less pronounced and eye contact is preserved. The improvement was found to be perceptually significant on video sequences coded at the ISDN rate of 64 kbps, with 48 kbps for the input (color) video signal in QCIF format.

We present a novel and practical way to integrate techniques from computer vision to low bit rate coding systems for video teleconferencing applications. Our focus is to locate and track the faces of persons in typical head-and-shoulders video sequences, and to exploit the face location information in a "classical" video coding/decoding system. The motivation is to enable the system to selectively encode various image areas and to produce psychologically pleasing coded images where faces are sharper. We refer to this approach as model-assisted coding. We propose a totally automatic, low-complexity algorithm, which robustly performs face detection and tracking. A priori assumptions regarding sequence content are minimal and the algorithm operates accurately even in cases of occlusion by moving objects. Face location information is exploited by a low bit rate 3D subband-based video coder which uses a model-assisted dynamic bit allocation with object-selective quantization. By transferring a small fraction of the total available bit rate from the non-facial to the facial area, the coder produces images with better-rendered facial features. The improvement was found to be perceptually significant on video sequences coded at 96 kbps for an input luminance signal in CIF format. The technique is applicable to any video coding scheme that allows for fine-grain quantizer selection (e.g. MPEG, H.261), and can maintain full decoder compatibility.>

An approach to image coding based on a fractal theory of iterated contractive transformations defined piecewise is described. The main characteristics of this approach are that it relies on the assumption that image redundancy can be efficiently captured and exploited through piecewise self-transformability on a block-wise basis, and it approximates an original image by a fractal image, obtained from a finite number of iterations of an image transformation called a fractal code. This approach is referred to as fractal block coding. The general coding-decoding system is based on the construction, for an image to be encoded, of a fractal code-a contractive image transformation for which the original image is an approximate fixed point-which, when applied iteratively on any initial image of the decoder, produces a sequence of images which converges to a fractal approximation of the original. The design of a system for the encoding of monochrome digital images at rates below 1 b/pixel is described. Ideas and extensions from the work of other researchers are presented.>

The author proposes an independent and novel approach to image coding, based on a fractal theory of iterated transformations. The main characteristics of this approach are that (i) it relies on the assumption that image redundancy can be efficiently exploited through self-transformability on a block-wise basis, and (ii) it approximates an original image by a fractal image. The author refers to the approach as fractal block coding. The coding-decoding system is based on the construction, for an original image to encode, of a specific image transformation-a fractal code-which, when iterated on any initial image, produces a sequence of images that converges to a fractal approximation of the original. It is shown how to design such a system for the coding of monochrome digital images at rates in the range of 0.5-1.0 b/pixel. The fractal block coder has performance comparable to state-of-the-art vector quantizers.

An approach to digital image coding, rooted in iterated transformation theory (ITT) and referred to as ITT-based coding, is proposed. It is a fractal block-coding method which relies on the assumption that image redundancy can be efficiently exploited through block self-transformability. The coding-decoding system is based on the construction, for any given original image to encode, of an image transformation of a special kind which (when iterated on any initial image) produces a sequence of images that converges to a fractal approximation of the original. The requirements on the transformation are that (i) it is contractive in the metric space of images endowed with the L/sub 2/ metric, (ii) it leaves the original image approximately invariant, and (iii) its complexity is smaller than that of the original image. The fully automated ITT-based system has comparable performance, in terms of signal-to-noise-ratio and bit rate, to state-or-the-art vector quantizers, with which it shares some features.>

The notion of fractal image compression arises from the fact that the iteration of simple deterministic mathematical procedures can generate images with infinitely intricate geometries, known as fracial images [2]. The purpose of research on fractal—based digital image coding is to solve the inverse problem of constraining this complexity to match the real—world complexity of real—world images. In this. paper, we propose a fractal image coding technique based on a mathematical theory of iterated transformations [1, 2, 3, 7] which encompasses deterministic fractal geometry. Initial results were reported in [7, 8]. The main characteristics of this technique are that (i) it is fractal in the sense that it approximates an original image by a fractal image, and (ii) it is a block—coding technique which relies on the assumption that image redundancy can be efficiently exploited through blockwise self—iransformabiliiy. We therefore refer to it as fracial block—coding. The coding—decoding system is based on the construction, for each original image given to encode, of a specific image transformation which, when iterated on any initial image, produces a sequence of images which converges to a fractal approximation of the original. We show how to design such coders, and thoroughly describe the implementation of a system for monochrome still images. Extremely promising coding results were obtained.