The Gabor expansion based spectrogram (commonly known as the Gabor spectrogram or the time-frequency distribution series) has previously been applied in many areas. In many applications, such as biomedical engineering, radar image processing, and seismology, it has been found to be a good alternative for the STFT-based spectrogram and Wigner-Ville distribution. Although the idea of the Gabor spectrogram is rather straightforward, the implementation has been quite involved. In this paper, we present a new algorithm for computing the Gabor spectrogram. The method presented is not only simple, but also fast. According to this new approach, the Gabor spectrogram is nothing more than separated two-dimensional interpolation filters. Consequently, it could be efficiently computed in the Fourier transform domain or implemented in off-the-shelf chips.

The chirp function is one of the most fundamental functions in nature. Many natural events can be roughly approximated by a group of chirp functions. In this paper, we present a practical adaptive chirplet based signal approximation algorithm. Unlike the other chirplet decompositions known so far, the elementary chirplet functions employed in this algorithm are adaptive. Therefore, the resulting approximation could better match the underlying signal and uses fewer coefficients. The effectiveness of the algorithm is demonstrated by numerical simulations.

Using the orthogonal-like Gabor (1946) expansion, we decompose the Wigner-Ville (1932, 1948) distribution as a linear combination of localized and oscillated 2D Gaussian functions. Based on the degree of oscillation, we further group those 2D Gaussian functions into time-frequency functions P/sub d/(t,/spl omega/) and thereby obtain the so-called time-frequency distribution series (TFDS). The TFDS/sub D/ consists of up to Dth order P/sub d/(t,/spl omega/). Since the influence of P/sub d/(t,/spl omega/) on those useful properties is inversely proportional to d, by adjusting the order D of the TFDS one could easily balance the cross-term interference and the useful properties.>

Visual pattern image sequence coding (VPISC) is a new digital image sequence (video) coding process that possesses significant advantages relative to other technologies; in particular, it is extremely efficient in terms of the computational effort required. It is designed to exploit properties of the human visual system (HVS), and thus yields high visual fidelity. Visual quality criteria are deliberately chosen over information-theoretic ones on the grounds that, in images intended for human viewing, visual criteria are the most meaningful ones. VPISC yields impressive compression comparable to other recent methods, such as motion-compensated vector quantization. VPISC divides the images into spatiotemporal cubes, which are then independently matched with one of a small, predetermined set of visually meaningful three-dimensional space-time patterns. The pattern set is chosen to conform to specific characteristics of the HVS. Also introduced are two modifications of VPISC: adaptive and foveal VPISC. These are spatiotemporally nonuniform implementations that code different portions of the image sequence at different resolutions, according to either a fidelity criterion (for AVPISC) or a foveation criterion (for FVPISC).>

A signal approximation via data-adaptive normalized Gaussian functions is presented. This approach resembles the traditional Gabor expansion, but it is more precise and efficient. Numerical simulations for the speech signal are included to demonstrate the effectiveness of the new scheme.>

A very fast high-compression, high-fidelity, image coding technique termed visual pattern image coding (VPIC) was recently introduced by the authors (1990). A hierarchical version of VPIC termed hierarchical VPIC (HVPIC) is developed here. In HVPIC, coded images of improved visual fidelity relative to VPIC are generated, with increased compressions (routinely exceeding 20:1). Significantly, the computational efficiency of simple VPIC is retained by HVPIC, which utilizes simple VPIC at each resolution level.>

Visual pattern image coding (VPIC) is a flexible image coding device offering excellent visual fidelity at high compression ratios with unprecedented, extremely low coding complexity. Hierarchical visual pattern image coding (HVPIC) schemes which achieve significantly increased compression (approaching 35:1) with improved fidelity and the very low complexity associated with VPIC are described. HVPIC employs standard VPIC embedded in a multiresolution (pyramid) structure. Low-resolution images coded at the upper levels augment coding of the higher-resolution images. Interactions between resolution levels is simple and efficient, yielding little increase in complexity relative to VPIC.>

Visual pattern image coding, or VPIC1-5, is an important new digital image coding process that possesses significant advantages relative to all other existing technologies: VPIC is capable of coding (i) with visual fidelity comparable to the best available techniques; (ii) at very high compressions exceeding the best available technologies: compressions in the range 30:1—40:1 are obtained routinely; (iii) with absolutely unprecedented coding efficiency - coding/decoding via VPIC is completely linear with respect to image size and entails a complexity 1-2 orders of magnitude faster than any prior high compression strategy; (iv) configurability. In the current work, the VPIC coding framework developed initially for single images is extended to image sequences. The algorithm for image sequence coding presented here, termed Visual Pattern Image Sequence Coding (or VPISC) exploits all of the advantages of "static" VPIC in the reduction of information from an additional (temporal) dimension to achieve unprecedented image sequence coding performance stated in terms of coding complexity, compression, and visual fidelity.

A novel framework for digital image compression called visual pattern image coding, or VPIC, is presented. In VPIC, set of visual-patterns is defined independent of the images to be coded. Each visual pattern is a subimage of limited spatial support that is visually meaningful to a normal human observer. The patterns are used as a basis for efficient image representation; since it is assumed that the images to be coded are natural optical images to be viewed by human observers, visual pattern design is developed using relevant psychophysical and physiological data. Although VPIC bears certain resemblances to block truncation (BTC) and vector quantification (VQ) image coding, there are important differences. First, there is no training phase required: the visual patterns derive from models of perceptual mechanisms; second, the assignment of patterns to image regions is not based on a standard (norm) error criterion; expensive search operations are eliminated.>