The data of interest are assumed to be represented as N-dimensional real vectors, and these vectors are compressible in some linear basis B, implying that the signal can be reconstructed accurately using only a small number M Lt N of basis-function coefficients associated with B. Compressive sensing is a framework whereby one does not measure one of the aforementioned N-dimensional signals directly, but rather a set of related measurements, with the new measurements a linear combination of the original underlying N-dimensional signal. The number of required compressive-sensing measurements is typically much smaller than N, offering the potential to simplify the sensing system. Let f denote the unknown underlying N-dimensional signal, and g a vector of compressive-sensing measurements, then one may approximate f accurately by utilizing knowledge of the (under-determined) linear relationship between f and g, in addition to knowledge of the fact that f is compressible in B. In this paper we employ a Bayesian formalism for estimating the underlying signal f based on compressive-sensing measurements g. The proposed framework has the following properties: i) in addition to estimating the underlying signal f, "error bars" are also estimated, these giving a measure of confidence in the inverted signal; ii) using knowledge of the error bars, a principled means is provided for determining when a sufficient number of compressive-sensing measurements have been performed; iii) this setting lends itself naturally to a framework whereby the compressive sensing measurements are optimized adaptively and hence not determined randomly; and iv) the framework accounts for additive noise in the compressive-sensing measurements and provides an estimate of the noise variance. In this paper we present the underlying theory, an associated algorithm, example results, and provide comparisons to other compressive-sensing inversion algorithms in the literature.

Bayesian Compressive Sensing

Compressive sensing (CS) is a framework whereby one performs N nonadaptive measurements to constitute a vector v isin RN used to recover an approximation u isin RM desired signal u isin RM with N 1 sets of compressive measurements {vi}i=1,L are performed, each of the associated {ui}i=1,Lare recovered one at a time, independently. In many applications the L ldquotasksrdquo defined by the mappings virarrui are not statistically independent, and it may be possible to improve the performance of the inversion if statistical interrelationships are exploited. In this paper, we address this problem within a multitask learning setting, wherein the mapping vrarru for each task corresponds to inferring the parameters (here, wavelet coefficients) associated with the desired signal vi, and a shared prior is placed across all of the L tasks. Under this hierarchical Bayesian modeling, data from all L tasks contribute toward inferring a posterior on the hyperparameters, and once the shared prior is thereby inferred, the data from each of the L individual tasks is then employed to estimate the task-dependent wavelet coefficients. An empirical Bayesian procedure for the estimation of hyperparameters is considered; two fast inference algorithms extending the relevance vector machine (RVM) are developed. Example results on several data sets demonstrate the effectiveness and robustness of the proposed algorithms.

Multitask Compressive Sensing

We propose an embedded, block-based, image wavelet transform coding algorithm of low complexity. It uses a recursive set-partitioning procedure to sort subsets of wavelet coefficients by maximum magnitude with respect to thresholds that are integer powers of two. It exploits two fundamental characteristics of an image transform-the well-defined hierarchical structure, and energy clustering in frequency and in space. The two partition strategies allow for versatile and efficient coding of several image transform structures, including dyadic, blocks inside subbands, wavelet packets, and discrete cosine transform (DCT). We describe the use of this coding algorithm in several implementations, including reversible (lossless) coding and its adaptation for color images, and show extensive comparisons with other state-of-the-art coders, such as set partitioning in hierarchical trees (SPIHT) and JPEG2000. We conclude that this algorithm, in addition to being very flexible, retains all the desirable features of these algorithms and is highly competitive to them in compression efficiency.

Efficient, low-complexity image coding with a set-partitioning embedded block coder

A teleconferencing system includes a network. The system includes a content node having content and an address in the network and in communication with the network. The system includes a first user node and a second user node in communication with each other through the network to form a conference. The first user node able to provide the address of the content node through the network to the first and second nodes so the first and second nodes can both access the content of the content node during the conference. A method for providing a teleconference call. A teleconferencing node.

Associating independent multimedia sources into a conference call

This chapter proposed a three dimensional set partitioned embedded block coder for hyperspectral image compression The three dimensional wavelet transform automatically exploits inter-band dependence Two versions of the algorithm were implemented The integer filter implementation enables lossy-to-lossless compression, and the floating point filter implementation provides better performance for lossy representation Wavelet packet structure and bit shifting were applied on the integer filter implementation to make the transform approximately unitary

/pdf/three-dimensional-wavelet-based-compression-of-hyperspectral-1v4v3g73bh.pdf

Three-Dimensional Wavelet-Based Compression of Hyperspectral Images

The invention concerns an electronic device (10) equipped with a video imaging process capability, which device includes a camera unit (11) arranged to produce image frames (I, FRC, FRP, FR, VFV1 - VFV3, FR1 - FR3, FR1' - FR3') from an imaging view which includes a region-of-interest ROI (T, T1 - T5), an adjustable optics (14) arranged in connection with the camera unit in order to focus the ROI on the camera unit, an identifier unit (CPU, 15) in order to identify a ROI from the image frame, a tracking unit (CPU, 16) in order to track the ROI from the image frames during the video imaging process and an auto-focus unit (12) arranged to analyze the ROI on the basis of the tracking results provided by the tracking unit in order to adjust the optics. The device is arranged to determine the spatial position of the ROI in the produced image frame without any estimation measures.

Method and device for controlling auto focusing of a video camera by tracking a region-of-interest

We present a low-complexity entropy coder originally designed to work in the JPEG2000 image compression standard framework. The algorithm is meant for embedded and non-embedded coding of wavelet coefficients inside a subband, and is called subband-block hierarchical partitioning (SBHP). It was extensively tested following the standard experiment procedures, and it was shown to yield a significant reduction in the complexity of entropy coding, with small loss in compression performance. Furthermore, it is able to seamlessly support all JPEG2000 features. We present a description of the algorithm, an analysis of its complexity, and a summary of the results obtained after its integration into the verification model (VM).

SBHP-a low complexity wavelet coder

An improved system and method for enabling the insertion, overlay, removal or replacement of sequential or concurrent targeted program segments and/or visual icons in a video bitstream without modifying the fidelity of the underlying visual media The present invention provides for a wide variety of supplemental enhancement information fields which permit the use of data updates that are synchronous with delivered video content The present invention offers a generic approach to program insertion and iconic overlay that covers a wide range of use-cases and applications, without necessarily transmitting the visual content to be inserted as part of the underlying visual media stream

Method for insertion and overlay of media content upon an underlying visual media

A method and system for allowing a client to carry out image surfing. After connecting with a server, the client can select an image category of images to surf from. Upon request by the client, the server sends a set of thumbnails of images to the client, allowing the client to specify one of them for surfing. The client has a GUI interface by which the client can move a selection box to select a portion of the specified image for viewing. If the client wishes to zoom in, zoom out, or enhance the quality of the viewed area, the client sends a request to the server so that the server can send the client the necessary image sub-component to achieve the zoomed or enhanced image portion.

Asad Islam

Papers

Efficient, low-complexity image coding with a set-partitioning embedded block coder

Method and device for controlling auto focusing of a video camera by tracking a region-of-interest

SBHP-a low complexity wavelet coder

Method for insertion and overlay of media content upon an underlying visual media

Method and device for image surfing