Support for evolutionary design approaches is not a very much investigated topic in the automation domain. Advanced facilities for semantic validation, refactoring and model transformations, which are necessary for rapid prototyping of control applications, are seldom found in design tools. Implementing such facilities is even more difficult when using the IEC 61499 standard, through some characteristics of the persistence model. This paper presents a specialized development environment for modelling and automatically generating applications for the industrial automation field, its unique facilities for supporting evolutionary design and the suggested improvements to the IEC 61499 model. This environment was developed inside a project for assessing the IEC 61499 principles and for verifying design approaches and execution models on a real plant.

Typical object detection algorithms on mobile cameras suffer from the lack of a-priori knowledge on the object to be detected. The variability in the shape, pose, color distribution, and behavior affect the robustness of the detection process. In general, such variability is addressed by using a large training data. However, only objects present in the training data can be detected. This paper introduces a vision-based system to address such problem. A master-slave approach is presented where a mobile camera (the slave) can match any object detected by a fixed camera (the master). Features extracted by the master camera are used to detect the object of interest in the slave camera without the use of any training data. A single observation is enough regardless of the changes in illumination, viewpoint, color distribution and image quality. A coarse to fine description of the object is presented built upon image statistics robust to partial occlusions. Qualitative and quantitative results are presented in an indoor and an outdoor urban scene.

A method that exploits an information theoretic framework to extract optimized audio features using video information is presented. A simple measure of mutual information (MI) between the resulting audio and video features allows the detection of the active speaker among different candidates. This method involves the optimization of an Mi-based objective function. No approximation is needed to solve this optimization problem, neither for the estimation of the probability density functions (pdfs) of the features, nor for the cost function itself. The pdfs are estimated from the samples using a nonparametric approach. The challenging optimization problem is solved using a global method: the differential evolution algorithm. Two information theoretic optimization criteria are compared and their ability to extract audio features specific to speech production is discussed. Using these specific audio features, candidate video features are then classified as member of the "speaker" or "non-speaker" class, resulting in a speaker detection scheme. As a result, our method achieves a speaker detection rate of 100% on in-house test sequences, and of 85% on most commonly used sequences.

We address the problem of motion estimation (ME) in digital video sequences and propose a new fast, adaptive, and efficient block-matching algorithm. Higher quality and efficiency are achieved using a statistical model for the motion vectors. This model introduces adaptation in the search window, drastically reducing the number of positions where correlation-type computation is performed. The efficiency is further improved by progressively undersampling the macroblock. Patterns for undersampling are proposed to obtain the maximum benefit from single instruction multiple data (SIMD) instructions. In contrast with existing motion-estimation techniques, search strategy and subsampled patterns are closely linked. This shows that a good search strategy is much more important than blindly reducing the number of pixels considered for the matching pattern. We describe an implementation of the proposed matching strategy that exploits the very long instruction word (VLIW) and SIMD technology available in the new Itanium processor family. Results show that the proposed algorithm adapts easily to the evolution of the scene avoiding annoying quality drops that can be observed with other deterministic algorithms. The total number of operations required by the proposed method is inferior to those required by traditional approaches.

The visual efficiency of an image compression technique depends directly on the amount of visually significant information it retains. By "visually significant" we mean information to which a human observer is most sensitive. The overall sensitivity depends on aspects such as contrast, color, spatial frequency, and so forth. One important aspect is the inverse relationship between contrast sensitivity and spatial frequency. This is described by the contrast sensitivity function (CSF). In compression algorithms the CSF can be exploited to regulate the quantization step-size to minimize the visibility of compression artifacts. Existing CSF implementations for wavelet-based image compression use the same quantization step-size for a large range of spatial frequencies. This is a coarse approximation of the CSF. This paper presents two new techniques that implement the CSF at significantly higher precision, adapting even to local variations of the spatial frequencies within a decomposition subband. The approaches can be used for luminance as well as color images. For color perception three different CSFs describe the sensitivity. The implementation technique is the same for each color band. Implemented into the JPEG2000 compression standard, the new techniques are compared to conventional CSF-schemes. The proposed techniques turn out to be visually more efficient than previously published methods. However, the emphasis of this paper is on how the CSF can be implemented in a precise and locally adaptive way, and not on the superior performance of these techniques.

Human observers are very sensitive to a loss of image texture in photo-realistic images. For example a portrait image without the fine skin texture appears unnatural. Once the image is decomposed by a wavelet transformation, this texture is represented by many wavelet coefficients of low- and medium-amplitude. The conventional encoding of all these coefficients is very bitrate expensive. Instead, such an unstructured or stochastic texture can be modeled by a noise process and be characterized with very few parameters. Thus, a hybrid scheme can be designed that encodes the structural image information by a conventional wavelet codec and the stochastic texture in a model-based manner. Such a scheme, called WITCH (Wavelet-based Image/Texture Coding Hybrid), is proposed. It implements such an hybrid coding approach, while nevertheless preserving the features of progressive and lossless coding. Its low computational complexity and the parameter coding costs of only 0.01 bpp make it a valuable extension of conventional codecs. A comparison with the JPEG2000 image compression standard showed that the WITCH-scheme achieves the same subjective quality while increasing the compression ratio by more than a factor of two.

We review recent advances in image retrieval. The two fundamental components of a retrieval system, representation and learning, are analyzed. Each component is decomposed into its constituent building blocks: features, feature representation, and similarity function for the representation; short and long-term procedures for learning. We identify a series of requirements for each of the sub-areas, e.g. optimality, invariance, perceptual relevance, computational tractability, and point out various approaches proposed to satisfy them. Several open problems are also identified.

The use of the discrete wavelet transform (DWT) for embedded lossy image compression is now well established. One of the possible implementations of the DWT is the lifting scheme (LS). Because perfect reconstruction is granted by the structure of the LS, nonlinear transforms can be used, allowing efficient lossless compression as well. The integer wavelet transform (IWT) is one of them. This is an interesting alternative to the DWT because its rate-distortion performance is similar and the differences can be predicted. This topic is investigated in a theoretical framework. A model of the degradations caused by the use of the IWT instead of the DWT for lossy compression is presented. The rounding operations are modeled as additive noise. The noise are then propagated through the LS structure to measure their impact on the reconstructed pixels. This methodology is verified using simulations with random noise as input. It predicts accurately the results obtained using images compressed by the well-known EZW algorithm. Experiment are also performed to measure the difference in terms of bit rate and visual quality. This allows to a better understanding of the impact of the IWT when applied to lossy image compression.

In this paper we propose a block matching algorithm (BM) that reduces the computational burden of the motion estimation in an hybrid video encoder. To decrease the global complexity it acts at three different levels represented by the frame itself, the searching window and the macroblock (MB). To achieve an optimal performance also onto current general purpose processors provided with SIMD register files, a particular sub-sampling pattern of the MB is used. The algorithm shows a lower complexity than the three step search (TSS). In sequences where the motion field is formed by large motion vectors, the quality performance can be better than the full search algorithm (FSA) with an a priori fixed maximum motion vector. The algorithm is in fact able to adapt itself to the sequence.

In this paper, the problem of progressive lossless image coding is addressed. A nonlinear decomposition for progressive lossless compression is presented. The decomposition into subbands is called rank-order polynomial decomposition (ROPD) according to the polynomial prediction models used. The decomposition method presented here is a further development and generalization of the morphological subband decomposition (MSD) introduced earlier by the same research group. It is shown that ROPD provides similar or slightly better results than the compared coding schemes such as the codec based on set partitioning in hierarchical trees (SPIHT) and the codec based on wavelet/trellis-coded quantization (WTCQ). Our proposed method highly outperforms the standard JPEG. The proposed lossless compression scheme has the functionality of having a completely embedded bit stream, which allows for data browsing. It is shown that the ROPD has a better lossless rate than the MSD but it has also a much better browsing quality when only a part of the bit stream is decompressed. Finally, the possibility of hybrid lossy/lossless compression is presented using ultrasound images. As with other compression algorithms, considerable gain can be obtained if only the regions of interest are compressed losslessly.

Digital images have become an important source of information in the modern world of communication systems. In their raw form, digital images require a tremendous amount of memory. Many research efforts have been devoted to the problem of image compression in the last two decades. Two different compression categories must be distinguished: lossless and lossy. Lossless compression is achieved if no distortion is introduced in the coded image. Applications requiring this type of compression include medical imaging and satellite photography. For applications such as video telephony or multimedia applications, some loss of information is usually tolerated in exchange for a high compression ratio. In this two-part paper, the major building blocks of image coding schemes are overviewed. Part I covers still image coding, and Part II covers motion picture sequences. In this first part, still image coding schemes have been classified into predictive, block transform, and multiresolution approaches. Predictive methods are suited to lossless and low-compression applications. Transform-based coding schemes achieve higher compression ratios for lossy compression but suffer from blocking artifacts at high-compression ratios. Multiresolution approaches are suited for lossy as well for lossless compression. At lossy high-compression ratios, the typical artifact visible in the reconstructed images is the ringing effect. New applications in a multimedia environment drove the need for new functionalities of the image coding schemes. For that purpose, second-generation coding techniques segment the image into semantically meaningful pairs. Therefore, parts of these methods have been adapted to work for arbitrarily shaped regions. In order to add another functionality, such as progressive transmission of the information, specific quantization algorithms must he defined. A final step in the compression scheme is achieved by the codeword assignment. Finally, coding results are presented which compare state-of-the-art techniques for lossy and lossless compression. The different artifacts of each technique are highlighted and discussed. Also, the possibility of progressive transmission is illustrated.

This paper addresses the issue of testing and quality assessment of motion rendition in digital video coding. Motion estimation and compensation are critical modules in video coders, as they are the most demanding resources and largely account for the visual quality of the resulting compressed stream. The testing of such modules is thus very important. A computational metric, based on a spatiotemporal model of the human visual system and of human motion sensing, is proposed and used to evaluate MPEG-2 compressed video. The metric is able to assess the quality of motion rendition and exhibits a good correlation with subjective data.

A novel decomposition scheme for image compression is presented. It is capable of applying any nonlinear model to compress images in a lossless way. Here, a very efficient polynomial model that considers spatial information as well as order statistic information is introduced. This new rank order polynomial decomposition (ROPD) that allows also for a progressive bitstream is applied to various images of different nature and compared to the morphological subband decomposition (MSD) and to the best prediction mode for lossless compression of the international standard, JPEG. For all compressed images, ROPD provides better compression results than MSD and clearly outperforms the lossless mode of JPEG.

This paper proposes a technique for spatio-temporal segmentation to identify the objects present in the scene represented in a video sequence. This technique processes two consecutive frames at a time. A region-merging approach is used to identify the objects in the scene. Starting from an oversegmentation of the current frame, the objects are formed by iteratively merging regions together. Regions are merged based on their mutual spatio-temporal similarity. We propose a modified Kolmogorov-Smirnov test for estimating the temporal similarity. The region-merging process is based on a weighted, directed graph. Two complementary graph-based clustering rules are proposed, namely, the strong rule and the weak rule. These rules take advantage of the natural structures present in the graph. Experimental results on different types of scenes demonstrate the ability of the proposed technique to automatically partition the scene into its constituent objects.

The compression of visual information in the framework of multimedia applications is discussed. To this end, major approaches to compress still as well as moving pictures are reviewed. The most important objective in any compression algorithm is that of compression efficiency. High-compression coding of still pictures can be split into three categories: waveform, second-generation, and fractal coding techniques. Each coding approach introduces a different artifact at the target bit rates. The primary objective of most ongoing research in this field is to mask these artifacts as much as possible to the human visual system. Video-compression techniques have to deal with data enriched by one more component, namely, the temporal coordinate. Either compression techniques developed for still images can be generalized for three-dimensional signals (space and time) or a hybrid approach can be defined based on motion compensation. The video compression techniques can then be classified into the following four classes: waveform, object-based, model-based, and fractal coding techniques. This paper provides the reader with a tutorial on major visual data-compression techniques and a list of references for further information as the details of each method.

We present a scheme for interactive video segmentation. A key feature of the system is the distinction between two levels of segmentation, namely, regions and object segmentation. Regions are homogeneous areas of the images, which are extracted automatically by the computer. Semantically meaningful objects are obtained through user interaction by grouping of regions according to the specific application. This splitting relieves the computer of ill-posed semantic problems, and allows a higher level of flexibility of the method. The extraction of regions is based on the multidimensional analysis of several image features by a spatially constrained fuzzy C-means algorithm. The local level of reliability of the different features is taken into account in order to adaptively weight the contribution of each feature to the segmentation process. Results on the extraction of regions as well as on the tracking of spatiotemporal objects are presented.

Cellular neural/nonlinear networks (CNN) are considered for efficient implementation of the most computationally intensive steps of dynamic image coding. Several analogic CNN algorithms are presented for the generation of binary image masks and image decomposition. Measurement results for the first CNN universal chips executing an analogic algorithm for a reconstruction operator are also presented. Based on measured execution times, the viability of the CNN implementation of efficient but computationally expensive compression algorithms such as dynamic image coding is assessed.

This paper presents the Swiss Federal Institute of Technology (EPFL) proposal to MPEG-4 video coding standardization activity. The proposed technique is based on a novel approach to audio-visual data compression entitled dynamic coding. The newly born multimedia environment supports a plethora of applications which cannot be covered adequately by a single compression technique. Dynamic coding offers the opportunity to combine several compression techniques and segmentation strategies. Given a particular application, these two degrees of freedom can be constrained and assembled in order to produce a particular profile which meets the set of specifications dictated by the application. The basic principles of this approach are presented together with the data representation system. The major characteristics of dynamic coding are reviewed, along with simulation results showing the performance of such an approach in a very low bit-rate video coding environment.

This paper introduces a novel approach to visual data compression. The approach, named dynamic coding, consists of an effective competition between several representation models used for describing data portions. The image data is represented as the union of several regions each approximated by a representation model locally appropriate. The dynamic coding concept leads to attractive features such as genericness, flexibility, and openness and is therefore particularly suited to a multimedia environment in which many types of applications are involved. Dynamic coding is a general proposal to visual data compression and many variations on the same theme may be designed. They differ by the particular procedure by which the data is segmented into objects and the local representation model selected. As an illustrative example, a video compression scheme based on the principles of dynamic coding is presented. This compression algorithm performs a joint optimization of the segmentation (restricted to a so-called generalized quadtree partition) together with the representation models associated with each data segment. Four representation models are competing namely, fractal, motion compensation, text and graphics, and background modes. Optimality is defined with respect to a rate-distortion tradeoff and the optimization procedure leads to a multicriterion segmentation.

In order to satisfy the needs of new multimedia applications, the problem of content-based video coding has to be addressed. A new approach of object interior coding is proposed. It is based on an arbitrarily-shaped subband transform followed by a generalized embedded zerotree wavelet algorithm. It is shown that the proposed technique achieves good compression results and has additional properties such as being computationally efficient, keeping the same dimensionality in the transformed domain, being perfect reconstruction, and allowing a perfect rate control. In addition a lossless mode can be defined by using an appropriate filter bank.

This paper addresses the problem of segmenting an image sequence in terms of multiple moving objects and tracking them through time and presents an object tracking algorithm. The objects are characterized through their temporal and spatial features so as to identify them and carry out the tracking procedure. The proposed tracking algorithm helps to detect the objects present in the current frame by supplying previous spatio-temporal information to the spatio-temporal segmentation procedure. In addition, the proposed algorithm tackles the correspondence problem. This is achieved through the use of a multiple hypotheses framework, the latter tests being based on both temporal and spatial characterizations of the objects.

This article addresses duality assessment of motion rendition in digital video coding. Motion estimation and compensation are critical modules in video coders. A computational metric, based on a spatio-temporal model of the human visual system and of human motion sensing, is proposed and used to evaluate MPEG-2 compressed video. The metric is able to assess the quality of motion rendition and exhibits a good correlation with subjective data.

This paper describes the current status of the image system engineering curriculum at the Swiss Federal Institute of Technology at Lausanne (Ecole Polytechnique Federale de Lausanne-EPFL). The responsibility of this curriculum is with the Signal Processing Laboratory of EPFL.

In this paper, we present an overview of the dynamic coding approach, together with recent developments carried out in this framework. Dynamic coding is a general approach to the problem of visual data representation in the context of multimedia. This approach consists of a dynamic combination of multiple representation models and segmentation strategies. Given an application, these two degrees of freedom are assembled so as to yield a specific profile which meets the specifications dictated by the application. The data is represented as the union of data segments, each described within a locally appropriate representation model. In order to illustrate this approach, a video compression system, based on the principles of dynamic coding, is proposed in the context of video-telephone/conference applications. This algorithm has been submitted to the MPEG-4 committee as a proposal for the first round of tests in November 1995. Recent developments have been added: in particular, a procedure enabling the generation of an object-oriented scalable bitstream is presented here. In order to reduce the blocking artifacts which are noticeable at high compression ratios, a post-processing technique is proposed.

This paper addresses the problem of the quantization noise reduction in subband image coding schemes. Two major artifacts occur for such coding schemes at high compression factors: the ringing effect around high-contrast contours and the blurred false contours in large smooth regions. The first distortion can be considerably reduced by an appropriate design of the subband filters. The second one can be eliminated by using the noise reduction technique proposed in this paper, which consists of applying a noise reduction filter to the DC subband. The advantages of this approach are as follows: first, it can be applied to any kind of subband decompositions; second, it removes quantization noise to which the eye is most sensitive; and third, it is computationally very efficient due to the small size (typically 64/spl times/64) of the DC subband. The colored quantization noise in the DC subband is rendered white by using the Roberts pseudonoise technique. The proposed noise reduction filter is a Wiener type filter with adaptive directional support. It has the advantage of reducing the noise without blurring the reconstructed image. It is shown that the proposed noise reduction filter augments the visual quality of the reconstructed image as well as its PSNR value.

In this paper we present a coding scheme for color images aiming at high compression ratios. It is based on perceptually classified vector quantization (VQ), where the different classes are chosen to achieve a better quality of the decoded image. Spatial correlation is reduced by a tree structured wavelet decomposition, then prediction of the insignificant coefficients is performed across subbands. Afterwards, the reduced set of data is organized in vectors in such a way that residual intra and inter band correlation are exploited. Finally, such vectors are coded by a classified VQ. Reconstructed images with good quality and a compression ratio higher that 100:1 have been produced by the proposed scheme, which has been shown to outperform the JPEG standard.

In video coding, the reduction of temporal redundancy is the key to achieving high performance. In the framework of sequence coding, motion estimation and compensation has been shown to be very efficient at removing temporal redundancy. The motion existing in a scene can be mainly seen as arising from local motions superimposed to the camera motion. A new two stage global/local motion estimation approach is presented. The global motion estimation only relies on the background information. It is based on a matching technique and the global motion model is chosen to be affine. Simulation results show significant improvements obtained with the proposed method compared to the usual methods.

Shape representation is an important image analysis task which can be used for contour coding and feature extraction. The morphological skeleton is a geometrical shape description by means of maximal inscribed structuring elements. The form of the structuring element is usually chosen a priori, and we show how genetic algorithms can be used for an automatic optimization of an arbitrary shaped structuring element. It permits improved progressive contour transmission and the extraction of shape features.

In this paper the problem of progressive lossless image coding is addressed. Many applications require a lossless compression of the image data. The possibility of progressive decoding of the bitstream adds a new functionality for those applications using data browsing. In practice, the proposed scheme can be of intensive use when accessing large databases of images requiring a lossless compression (especially for medical applications). The international standard JPEG allows a lossless mode. It is based on an entropy reduction of the data using various kinds of estimators followed by source coding. The proposed algorithm works with a completely different philosophy summarized in the following four key points: 1) a perfect reconstruction hierarchical morphological subband decomposition yielding only integer coefficients, 2) prediction of the absence of significant information across scales using zerotrees of wavelet coefficients, 3) entropy-coded successive-approximation quantization, and 4) lossless data compression via adaptive arithmetic coding. This approach produces a completely embedded bitstream. Thus, it is possible to decode only partially the bitstream to reconstruct an approximation of the original image.

A morphological subband decomposition with perfect reconstruction is proposed. Critical subsampling is achieved. The reconstructed images using this decomposition do not suffer from any ringing effect. In order to avoid poor texture representation by the morphological filters an adaptive subband decomposition is introduced. It chooses linear filters on textured regions and morphological filters otherwise. A simple and efficient texture detection criterion is proposed and applied to the adaptive decomposition. Comparisons to other coding techniques such as JPEG and linear subband coding show that the proposed scheme performs significantly better both in terms of PSNR and visual quality.>

Region based coding schemes are among the most promising compression techniques for very low bit-rates. They consist of image segmentation, contour and texture coding. In this paper, a new shape representation for segmented images based on the geodesic morphological skeleton is presented. It is used for the coding of contour prediction residues obtained after motion compensation based on temporally modeled motion vectors. A non-reversible pre-skeletonization filter removes contour noise. A reversible post-skeletonization filter allows progressive transmission of contour information. It is based on the spatial distribution of the skeleton points, which are found to be close to known contours.>

In order to solve the bottleneck problem of region-based coding scheme, which is the contour coding, lossy methods are introduced in this paper for both 2D and 3D (2D plus time) contour image(s). A non-linear filter by means of majority operations is designed to act as contour simplification for both pre-processing and post-processing of contour coding. A 20% gain in contour coding is achieved by applying this filter while the contour location error is within one pixel width. Afterwards, an efficient contour motion estimation and compensation coding method is presented. A fast full search motion estimation algorithm is also described, which is 2R times faster than traditional method (R is the region number). Experimental results have demonstrated outstanding performance for very low bit rate video coding.>

In this article we present an image processing based method for intrusion detection. The algorithm is characterized by a low computational cost, a high sensitivity to the presence of objects, robustness to illumination changes and modest memory requirements. The suggested approach is to separate the moving object edges from the background.

This paper describes a segmentation-based coding algorithm for very low bit rate. The algorithm involves a segmentation relying on a time recursive morphological segmentation technique. It extracts both large and contrasted regions. Then, a simple region-based motion estimation is performed. Finally, the sequence is coded by contour-texture motion compensation. The proposed method leads to very low bit rates while being able to represent the meaningful regions of the scene. Moreover, it makes no assumption about the scene content and can be used for any sequence.

In this paper we propose a complete subband-based codec for image compression purposes, using a new 2D algorithm based on zero-crossing and energy information. Although signal representation based on sign information is attractive from a coding point of view, two major problems inhibited its practical use, namely the large amount of side information needed for obtaining a stable reconstruction and the efficient treatment of inherently 2D signals. The new algorithm significantly improves the reconstruction results obtained in our previous work, thanks to its improved convergence and its capabilities to deal with the two-dimensional nature of the subband image signals. The zero-crossing information is compressed in a lossless way using the JBIG standard and the energy information in a lossy way using an arithmetic coder. Good results have been obtained both at the reconstruction and at the coding level, which show the feasibility of the global scheme.

The general subband decomposition problem is discussed. A fast and optimally localized biorthogonal wavelet transformation, suitable for image compression applications, is proposed. The performance of this transformation is compared to that of the discrete cosine transform in the context of still image compression.>

A new segmentation algorithm for still black and white images is introduced. This algorithm forms the basis of a region-oriented sequence coding technique, currently under development. The algorithm models the human mechanism of selecting regions both by their interior characteristics and their boundaries. This is carried out in two different stages: with a preprocessing that takes into account only gray level information, and with a stochastic model for segmented images that uses both region interior and boundary information. In the stochastic model, the gray level information within the regions is modeled by stationary Gaussian processes, and the boundary information by a Gibbs-Markov random field (GMRF). The segmentation is carried out by finding the most likely realization of the joint process (maximum a posteriori criterion), given the preprocessed image. For decreasing the computational load while avoiding local maxima in the probability function, suboptimal versions of the algorithm are proposed.>

The authors present a parametric probability density estimation technique for Markov processes defined by a first-order nonlinear autoregressive equation. It is based on the approximation of these processes as sampled versions of the continuous-time solutions of stochastic differential equations (SDEs) via the discretization scheme presented by T. Ozaki (1985). First, a polynomial estimate of the nonlinear recursion function is obtained from the data and then a suitable transformation of its coefficients is performed in order to obtain an estimate of the function in the corresponding SDE. The PDF estimate is then the equilibrium PDF of this SDE.>

A method is described for reducing the information contained in an image sequence, while retaining the information necessary for the interpretation of the sequence by a human observer. The method consists of first locating the redundant information, reducing the degree of redundancy, and coding the result. The sequence is treated as a single 3D data volume, the voxels of which are grouped into several regions, obtained by a 3D split and merge algorithm. The information is coded by representing the borders of the regions using a pyramidal structure in the x, y, t space. The coefficients of the approximating polynomials are coded in a straightforward manner. For 256*256 pixel, 25 frame/s image sequences, compressions allowing transmission rates near 64 kbit/s are obtained.>

An orthogonal transform using polar separable filters is introduced. The conditions for perfect reconstruction properties to hold will be discussed. A method for constructing orthogonal pairs of directional operators is derived. An example of a two band orthogonal perfect reconstruction fan filter pair is presented. An orthogonal directional four band filter bank is shown

A compression technique based on an expansion is presented. The elementary functions of the expansion form a class of pyramidal Gabor functions covering the frequency domain in octave bands. The image sequence is coded by differentially coding selected coefficients of this expansion. Simulation results show sequences reconstructed with good quality for a bit rate less than 64 Kbit/s.

The digital representation of an image requires a very large number of bits. The goal of image coding is to reduce this number, as much as possible, and to reconstruct a faithful duplicate of the original picture. Early efforts in image coding, solely guided by information theory, led to a plethora of methods. The compression ratio reached a plateau around 10: 1 a couple of years ago. Recent progress in the study of the brain mechanism of vision and scene analysis has opened new vistas in picture coding. Directional sensitivity of the neurones in the visual pathway combined with the separate processing of contours and textures has led to a new class of coding methods capable of achieving compression ratios as high as 100: 1. This paper presents recent progress on some of the main avenues of object-based methods. These second generation techniques make use of contour-texture modeling, new results in neurophysiology and psychophisics and scene analysis.

This correspondence describes an improved computational algorithm for the Hadamard transform and the R transform. By performing the computation "in place", the number of storage locations is minimized and the speed is increased. The transformed coefficients are in the order of increasing sequency.