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This paper presents a solution to the video prediction problem based on the deep learning paradigm. Predicted frames generated by existing video prediction models are often blurry and have difficulty maintaining accuracy in multi-step prediction. To overcome these limitations, this paper presents a deep learning model, named the deep pixel restoration loop coding network (DPR-LC-Net), which employs the concept of predictive coding and adopts pixels in the real frames. While making a long-term prediction, it can generate clear prediction frames with few errors. The calculation process of DPR-LC-Net is multi-sequential: it completes calculations in the form of an approximate loop from top to bottom and then from left to right. After predicting subsequent steps, DPR-LC-Net both calculates errors and removes them from sequential prediction. Finally, the model includes a unique pixel restoration module that works efficiently on pixels in the preceding real frames to generate predicted frames, thereby improving the clarity of the predicted frames. Extensive experiments using four video datasets illustrate that the prediction performance of DPR-LC-Net is superior to that of the state-of-the-art models. [ABSTRACT FROM AUTHOR]

Hyperspectral anomaly detection aims to fast and credibly find nontrivial candidate targets without prior knowledge, which has become an increasingly pressing need as imagery swath and resolution are growing rapidly. Relevant state-of-the-art learning-based anomaly detection approaches have benefited from data-driven hierarchical feature embeddings that typically model the geometric distribution of spectral vectors. However, most of these techniques are incompatible with resource-constrained applications: 1) huge computational costs caused by feedforward convolution operation cannot be supported with limited computation resources and storage (e.g., in-orbit processing); 2) detection accuracy relies on large-scale training datasets, which results in labor-expensive imagery collection, pixel-level labeling, and time-consuming learning procedure. To address these issues, we advocate a learning-free, frequency domain anomaly detection method combined with predictive coding, an intriguing heuristic prior from human vision system. Technically, 1) inherently efficient frequency transformation could be implemented with existing image compression modules (e.g., JPEG or JPEG2000 codec), which improves the utilization of computational resources; 2) predictive coding mechanism is exploited for suppressing frequently occurring information represented in the low-entropy frequency domain, such that the “unpredictive” subject (i.e., anomaly spectrums) can “pop out” with naive residuals. Such heuristic prior incorporated into the computational model can reduce dependence on the large-scale training set. Experiments on real-world hyperspectral datasets confirm the efficacy of our model. Besides, low computation cost of the proposed (fast frequency transformation and analytical solutions) anomaly detector facilitates rather straightforward sliding window verification in high-resolution imagery.

Federated learning can enable remote workers to collaboratively train a shared machine learning model while allowing training data to be kept locally. In the use case of wireless mobile devices, the communication overhead is a critical bottleneck due to limited power and bandwidth. Prior work has utilized various data compression tools such as quantization and sparsification to reduce the overhead. In this paper, we propose a predictive coding based compression scheme for federated learning. The scheme has shared prediction functions among all devices and allows each worker to transmit a compressed residual vector derived from the reference. In each communication round, we select the predictor and quantizer based on the rate–distortion cost, and further reduce the redundancy with entropy coding. Extensive simulations reveal that the communication cost can be reduced up to 99% with even better learning performance when compared with other baseline methods. [ABSTRACT FROM AUTHOR]

This paper presents a new lossless image compression method based on the learning of pixel values and contexts through multilayer perceptrons (MLPs). The prediction errors and contexts obtained by MLPs are forwarded to adaptive arithmetic encoders, like the conventional lossless compression schemes. The MLP-based prediction has long been attempted for lossless compression, and recently convolutional neural networks (CNNs) are also adopted for the lossy/lossless coding. While the existing MLP-based lossless compression schemes focused only on accurate pixel prediction, we jointly predict the pixel values and contexts. We also adopt and design channel-wise progressive learning, residual learning, and duplex network in this MLP-based framework, which leads to improved coding gain compared to the conventional methods. Experiments show that the proposed method performs better than the conventional non-learning algorithms and also recent learning-based compression methods with practical computation time.

Stereoscopic image quality measurement (SIQM) has become increasingly important for guiding stereo image processing and commutation systems due to the widespread usage of 3-D contents. Compared with conventional methods that are relied on handcrafted features, deep-learning-oriented measurements have achieved remarkable performance in recent years. However, most existing deep SIQM evaluators are not specifically built for stereoscopic contents and consider little prior domain knowledge of the 3-D human visual system (HVS) in network design. In this article, we develop a Predictive Auto-encoDing Network (PAD-Net) for blind/no-reference SIQM. In the first stage, inspired by the predictive coding theory that the cognition system tries to match bottom–up visual signal with top–down predictions, we adopt the encoder–decoder architecture to reconstruct the distorted inputs. Besides, motivated by the binocular rivalry phenomenon, we leverage the likelihood and prior maps generated from the predictive coding process in the Siamese framework for assisting SIQM. In the second stage, a quality regression network is applied to the fusion image for acquiring the perceptual quality prediction. The performance of PAD-Net has been extensively evaluated on three benchmark databases and the superiority has been well validated on both symmetrically and asymmetrically distorted stereoscopic images under various distortion types. [ABSTRACT FROM AUTHOR]

The human visual system (HVS) is a hierarchical system, in which visual signals are processed hierarchically. In this paper, the HVS is modeled as a three-level communication system and visual perception is divided into three stages according to the hierarchical predictive coding theory. Then, a novel just noticeable distortion (JND) estimation scheme is proposed. In visual perception, the input signals are predicted constantly and spontaneously in each hierarchy, and neural response is evoked by the central residue and inhibited by surrounding residues. These two types’ residues are regarded as the positive and negative visual incentives which cause positive and negative perception effects, respectively. In neuroscience, the effect of incentive on observer is measured by the surprise of this incentive. Thus, we propose a surprise-based measurement method to measure both perception effects. Specifically, considering the biased competition of visual attention, we define the product of the residue self-information (i.e., surprise) and the competition biases as the perceptual surprise to measure the positive perception effect. As for the negative perception effect, it is measured by the average surprise (i.e., the local Shannon entropy). The JND threshold of each stage is estimated individually by considering both perception effects. The total JND threshold is finally obtained by non-linear superposition of three stage thresholds. Furthermore, the proposed JND estimation scheme is incorporated into the codec of Versatile Video Coding for image compression. Experimental results show that the proposed JND model outperforms the relevant existing ones, and over 16% of bit rate can be reduced without jeopardizing the perceptual quality. [ABSTRACT FROM AUTHOR]

Deep learning, such as convolutional neural networks, has been achieved great success in image processing, computer vision task, and image compression, and has achieved better performance. This paper designs a multiple description coding frameworks based on symmetric convolutional auto-encoder, which can achieve high-quality image reconstruction. First, the image is input into the convolutional auto-encoder, and the extracted features are obtained. Then, the extracted features are encoded by the multiple description coding and split into two descriptions for transmission to the decoder. We can get the side information by the side decoder and the central information by the central decoder. Finally, the side information and the central information are deconvolved by convolutional auto-encoder. The experimental results validate that the proposed scheme outperforms the state-of-the-art methods.

Data acquired over long periods of time like High Definition (HD) videos or records from a sensor over long time intervals, have to be efficiently compressed, to reduce their size. The compression has also to allow efficient access to random parts of the data upon request from the users. Efficient compression is usually achieved with prediction between data points at successive time instants. However, this creates dependencies between the compressed representations, which is contrary to the idea of random access. Prediction methods rely in particular on reference data points, used to predict other data points. The placement of these references balances compression efficiency and random access. Existing solutions to position the references use ad hoc methods. In this paper, we study this joint problem of compression efficiency and random access. We introduce the storage cost as a measure of the compression efficiency and the transmission cost for the random access ability. We express the reference placement problem that trades storage with transmission cost as an integer linear programming problem. Considering additional assumptions on the sources and coding methods reduces the complexity of the search space of the optimization problem. Moreover, we show that the classical periodic placement of the references is optimal, when the encoding costs of each data point are equal and when requests of successive data points are made. In this particular case, a closed-form expression of the optimal period is derived. Finally, the proposed optimal placement strategy is compared with an ad hoc method, where the references correspond to sources where the prediction does not help reducing significantly the encoding cost. The proposed optimal algorithm shows a bit saving of −20% with respect to the ad hoc method. [ABSTRACT FROM AUTHOR]

In many state-of-the-art compression systems, signal transformation is an integral part of the encoding and decoding process, where transforms provide compact representations for the signals of interest. This paper introduces a class of transforms called graph-based transforms (GBTs) for video compression, and proposes two different techniques to design GBTs. In the first technique, we formulate an optimization problem to learn graphs from data and provide solutions for optimal separable and nonseparable GBT designs, called GL-GBTs. The optimality of the proposed GL-GBTs is also theoretically analyzed based on Gaussian-Markov random field (GMRF) models for intra and inter predicted block signals. The second technique develops edge-adaptive GBTs (EA-GBTs) in order to flexibly adapt transforms to block signals with image edges (discontinuities). The advantages of EA-GBTs are both theoretically and empirically demonstrated. Our experimental results show that the proposed transforms can significantly outperform the traditional Karhunen-Loeve transform (KLT). [ABSTRACT FROM AUTHOR]

3D point clouds associated with attributes are considered as a promising paradigm for immersive communication. However, the corresponding compression schemes for this media are still in the infant stage. Moreover, in contrast to conventional image/video compression, it is a more challenging task to compress 3D point cloud data, arising from the irregular structure. In this paper, we propose a novel and effective compression scheme for the attributes of voxelized 3D point clouds. In the first stage, an input voxelized 3D point cloud is divided into blocks of equal size. Then, to deal with the irregular structure of 3D point clouds, a geometry-guided sparse representation (GSR) is proposed to eliminate the redundancy within each block, which is formulated as an $\ell _{0}$ -norm regularized optimization problem. Also, an inter-block prediction scheme is applied to remove the redundancy between blocks. Finally, by quantitatively analyzing the characteristics of the resulting transform coefficients by GSR, an effective entropy coding strategy that is tailored to our GSR is developed to generate the bitstream. Experimental results over various benchmark datasets show that the proposed compression scheme is able to achieve better rate-distortion performance and visual quality, compared with state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This paper aims to investigate how adequate cognitive functions for recognizing, predicting, and generating a variety of actions can be developed through iterative learning of action-caused dynamic perceptual patterns. Particularly, we examined the capabilities of mental simulation of one’s own actions as well as the inference of others’ intention because they play a crucial role, especially in social cognition. We propose a dynamic neural network model based on predictive coding which can generate and recognize dynamic visuo-proprioceptive patterns. The proposed model was examined by conducting a set of robotic simulation experiments in which a robot was trained to imitate visually perceived gesture patterns of human subjects in a simulation environment. The experimental results showed that the proposed model was able to develop a predictive model of imitative interaction through iterative learning of large-scale spatio-temporal patterns in visuo-proprioceptive input streams. Also, the experiment verified that the model was able to generate mental imagery of dynamic visuo-proprioceptive patterns without feeding the external inputs. Furthermore, the model was able to recognize the intention of others by minimizing prediction error in the observations of the others’ action patterns in an online manner. These findings suggest that the error minimization principle in predictive coding could provide a primal account for the mirror neuron functions for generating actions as well as recognizing those generated by others in a social cognitive context. [ABSTRACT FROM AUTHOR]

Shape is a popular way to define objects and shape coding is a key technique for object-based 3D video applications. In this paper, the issue of efficient shape coding for object-based 3D video applications is addressed, and a novel contour-based and chain-represented scheme is proposed. For a given 3D shape video, contour extraction and preprocessing are first implemented followed by chain-based representation. Then, to achieve high coding efficiency, a chain-based prediction and compensation technique is developed based on joint motion-compensated prediction and disparity-compensated prediction to effectively exploit the intra-view temporal correlation and the inter-view spatial correlation. Experiments are conducted, and the results demonstrate that the proposed scheme is more efficient than the existing methods, including state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In this paper, we propose to use a self-adaptive redundant dictionary, consisting of all possible inter and intra prediction candidates, to directly represent the frame blocks in a video sequence. The self-adaptive dictionary generalizes the conventional predictive coding approach by allowing adaptive linear combinations of prediction candidates, which is solved by an rate-distortion aware L0-norm minimization problem using orthogonal least squares (OLS). To overcome the inefficiency in quantizing and coding coefficients corresponding to correlated chosen atoms, we orthonormalize the chosen atoms recursively as part of OLS process. We further propose a two-stage video coding framework, in which a second stage codes the residual from the chosen atoms using a modified discrete cosine transform (DCT) dictionary that is adaptively orthonormalized with respect to the subspace spanned by the first stage atoms. To determine the transition from the first stage to the second stage, we propose a rate-distortion (RD) aware adaptive switching algorithm. The proposed framework is further extended to accommodate variable block sizes ($16\times 16$ , $8\times 8$ , and $4\times 4$), and the partition mode is derived by a fast partition mode decision algorithm. A context-adaptive binary arithmetic entropy coder is designed to code the symbols of the proposed coding framework. The proposed coder shows competitive, and in some cases better RD performance, compared with the HEVC video coding standard for P-frames. [ABSTRACT FROM AUTHOR]

Spectral redundancy is a key element to be exploited in compression of remote sensing data. Combined with an entropy encoder, it can achieve competitive lossless coding performance. One of the latest techniques to decorrelate the spectral signal is the regression wavelet analysis (RWA). RWA applies a wavelet transform in the spectral domain and estimates the detail coefficients through the approximation coefficients using linear regression. RWA was originally coupled with JPEG 2000. This letter introduces a novel coding approach, where RWA is coupled with the predictor of CCSDS-123.0-B-1 standard and a lightweight contextual arithmetic coder. In addition, we also propose a smart strategy to select the number of RWA decomposition levels that maximize the coding performance. Experimental results indicate that, on average, the obtained coding gains vary between 0.1 and 1.35 bits-per-pixel-per-component compared with the other state-of-the-art coding techniques. [ABSTRACT FROM AUTHOR]

We propose a new coding technique for sequential transmission of a stream of Gauss–Markov sources over erasure channels under a zero decoding delay constraint. Our proposed scheme is a combination (hybrid) of predictive coding with truncated memory, and quantization-and-binning. We study the optimality of our proposed scheme using an information theoretic model. In our setup, the encoder observes a stream of source vectors that are spatially independent and identically distributed (i.i.d.) and temporally sampled from a first-order stationary Gauss–Markov process. The channel introduces an erasure burst of a certain maximum length $B$ , starting at an arbitrary time, not known to the transmitter. The reconstruction of each source vector at the destination must be with zero delay and satisfy a quadratic distortion constraint with an average distortion of $D$ . The decoder is not required to reconstruct those source vectors that belong to the period spanning the erasure burst and a recovery window of length $W$ following it. We study the minimum compression rate $R(B,W,D)$ in this setup. As our main result, we establish upper and lower bounds on the compression rate. The upper bound (achievability) is based on our hybrid scheme. It achieves significant gains over baseline schemes such as (leaky) predictive coding, memoryless binning, a separation-based scheme, and a group of pictures-based scheme. The lower bound is established by observing connection to a network source coding problem. The bounds simplify in the high resolution regime, where we provide explicit expressions whenever possible, and identify conditions when the proposed scheme is close to optimal. We finally discuss the interplay between the parameters of our burst erasure channel and the statistical channel models and explain how the bounds in the former model can be used to derive insights into the simulation results involving the latter. In particular, our proposed scheme outperforms the baseline schemes over the i.i.d. erasure channel and the Gilbert–Elliott channel, and achieves performance close to a lower bound in some regimes. [ABSTRACT FROM PUBLISHER]

Predictive coding is attractive for compression of hyperspectral images onboard of spacecrafts in light of the excellent rate-distortion performance and low complexity of recent schemes. In this letter, we propose a rate control algorithm and integrate it in a lossy extension to the CCSDS-123 lossless compression recommendation. The proposed rate algorithm overhauls our previous scheme by being orders of magnitude faster and simpler to implement, while still providing the same accuracy in terms of output rate and comparable or better image quality. [ABSTRACT FROM PUBLISHER]

This paper addresses the problem of efficient predictive lossless compression on the regions of interest (ROIs) in the hyperspectral images with no-data regions. We propose a two-stage prediction scheme, where a context-similarity-based weighted average prediction is followed by recursive least square filtering to decorrelate the hyperspectral images for compression. We then propose to apply separate Golomb–Rice codes for coding the prediction residuals of the full-context pixels and boundary pixels, respectively. To study the coding gains of this separate coding scheme, we introduce a mixture geometric model to represent the residuals associated with various combinations of the full-context pixels and boundary pixels. Both information-theoretic analysis and simulations on synthetic data confirm the advantage of the separate coding scheme over the conventional coding method based on a single underlying geometric distribution. We apply the aforementioned prediction and coding methods to four publicly available hyperspectral image data sets, attaining significant improvements over several other state-of-the-art methods, including the shape-adaptive JPEG 2000 method. [ABSTRACT FROM PUBLISHER]

It is well known that Shannon’s rate-distortion function (RDF) in the colored quadratic Gaussian (QG) case can be parametrized via a single Lagrangian variable (the water level in the reverse water filling solution). In this paper, we show that the symmetric colored QG multiple description (MD) RDF in the case of two descriptions can be parametrized in the spectral domain via two Lagrangian variables, which control the tradeoff between the side distortion, the central distortion, and the coding rate. This spectral-domain analysis is complemented by a time-domain scheme-design approach: we show that the symmetric colored QG MD RDF can be achieved by combining ideas of delta–sigma modulation and differential pulse-code modulation. In particular, two source prediction loops, one for each description, are embedded within a common noise-shaping loop, whose parameters are explicitly found from the spectral-domain characterization. [ABSTRACT FROM AUTHOR]

Independent component analysis (ICA) is a statistical method for transforming an observable multi-dimensional random vector into components that are as statistically independent as possible from each other. Usually, the ICA framework assumes a model according to which the observations are generated (such as a linear transformation with additive noise). ICA over finite fields is a special case of ICA in which both the observations and the independent components are over a finite alphabet. In this paper, we consider a generalization of this framework in which an observation vector is decomposed to its independent components (as much as possible) with no prior assumption on the way it was generated. This generalization is also known as Barlow’s minimal redundancy representation problem and is considered an open problem. We propose several theorems and show that this hard problem can be accurately solved with a branch and bound search tree algorithm, or tightly approximated with a series of linear problems. Our contribution provides the first efficient set of solutions to Barlow’s problem. The minimal redundancy representation (also known as factorial code) has many applications, mainly in the fields of neural networks and deep learning. The binary ICA is also shown to have applications in several domains, including medical diagnosis, multi-cluster assignment, network tomography, and internet resource management. In this paper, we show that this formulation further applies to multiple disciplines in source coding, such as predictive coding, distributed source coding, and coding of large alphabet sources. [ABSTRACT FROM AUTHOR]

As image-based 3-D modeling is used in a variety of applications, accordingly, the compression of 3-D object geometry represented by multiple images becomes an important task. This paper presents a model-based approach to predict the geometric structure of an object using its visual hull. A visual hull is a geometric entity generated by shape-from-silhouette (SFS), and consequently it largely follows the overall shape of the object. The construction of a visual hull is computationally inexpensive and a visual hull can be encoded with relatively small amount of bits because it can be represented with 2-D silhouette images. Therefore, when it comes to the predictive compression of object’s geometric data, the visual hull should be an effective predictor. In the proposed method, the geometric structure of an object is represented by a layered depth image (LDI), and a visual hull from the LDI data is computed via silhouette generation and SFS. The geometry of an object is predicted with the computed visual hull, and the visual hull data with its prediction errors are encoded. Simulation results show that the proposed predictive coding based on visual hull outperforms the previous image-based methods and the partial surface-based method. [ABSTRACT FROM PUBLISHER]

The 3D video extension of High Efficiency Video Coding (3D-HEVC) exploits texture-depth redundancies in 3D videos using intercomponent coding tools. It also inherits the same quadtree coding structure as HEVC for both components. The current software implementation of 3D-HEVC includes encoder shortcuts that speed up the quadtree construction process, but those are always accompanied by coding losses. Furthermore, since the texture and its associated depth represent the same scene, at the same time instant and view point, their quadtrees are closely linked. In this paper, an intercomponent tool is proposed in which this link is exploited to save both runtime and bits through a joint coding of the quadtrees. If depth is coded before the texture, the texture quadtree is initialized from the coded depth quadtree. Otherwise, the depth quadtree is limited to the coded texture quadtree. A 31% encoder runtime saving, a −0.3% gain for coded and synthesized views and a −1.8% gain for coded views are reported for the second method. [ABSTRACT FROM AUTHOR]

This paper proposes a lossless to lossy compression scheme for hyperspectral images based on dual-tree Binary Embedded Zerotree Wavelet (BEZW) algorithm. The algorithm adapts Karhunen-Loève Transform and Discrete Wavelet Transform to achieve 3-D integer reversible hybrid transform and decorrelate spectral and spatial data. Since statistics of the hyperspectral image are not symmetrical, the asymmetrical dual-tree structure is introduced. The 3-D BEZW algorithm compresses hyperspectral images by implementing progressive bitplane coding. The lossless and lossy compression performance is compared with other state-of-the-art predictive coding and transform-based coding algorithms on Airborne Visible/Infrared Imaging Spectrometer images. Results show that the 3-D-BEZW lossless compression performance is comparable with the best predictive algorithms, while its computational cost is comparable with those of transform-based algorithms. [ABSTRACT FROM PUBLISHER]

In this paper, two multiple description coding schemes are developed, based on prediction-induced randomly offset quantizers and unequal-deadzone-induced near-uniformly offset quantizers, respectively. In both schemes, each description encodes one source subset with a small quantization stepsize, and other subsets are predictively coded with a large quantization stepsize. In the first method, due to predictive coding, the quantization bins that a coefficient belongs to in different descriptions are randomly overlapped. The optimal reconstruction is obtained by finding the intersection of all received bins. In the second method, joint dequantization is also used, but near-uniform offsets are created among different low-rate quantizers by quantizing the predictions and by employing unequal deadzones. By generalizing the recently developed random quantization theory, the closed-form expression of the expected distortion is obtained for the first method, and a lower bound is obtained for the second method. The schemes are then applied to lapped transform-based multiple description image coding. The closed-form expressions enable the optimization of the lapped transform. An iterative algorithm is also developed to facilitate the optimization. Theoretical analyzes and image coding results show that both schemes achieve better performance than other methods in this category. [ABSTRACT FROM AUTHOR]

In this paper, we propose a fixed point theoretical analysis of motion compensated predictive coding and demonstrate its potential in encoder optimization. Through viewing the encoder-decoder pair as a nonlinear dynamical system and inquiring about its convergent property, we demonstrate the feasibility of approximating the fixed point through recursive coding both theoretically and experimentally. Such a recursive coding approach to encoder optimization admits an interpretation of finding a more compact representation through local perturbation on the perceptual manifold. Experimental results have shown that our approach can achieve bit savings of 5-40\% without sacrificing visual quality when tested on the KTA implementation of H.264 (JM14.2). [ABSTRACT FROM PUBLISHER]

We introduce two new source coding problems: robust sequential coding and robust predictive coding. For the Gauss–Markov source model with the mean squared error distortion measure, we characterize certain supporting hyperplanes of the rate region of these two coding problems. Our investigation also reveals an information-theoretic minimax theorem and the associated extremal inequalities. [ABSTRACT FROM PUBLISHER]

An efficient fine-granular scalable coding algorithm of 3-D mesh sequences for low-latency streaming applications is proposed in this work. First, we decompose a mesh sequence into spatial and temporal layers to support scalable decoding. To support the finest-granular spatial scalability, we decimate only a single vertex at each layer to obtain the next layer. Then, we predict the coordinates of decimated vertices spatially and temporally based on a hierarchical prediction structure. Last, we quantize and transmit the spatio-temporal prediction residuals using an arithmetic coder. We propose an efficient context model for the arithmetic coding. Experiment results show that the proposed algorithm provides significantly better compression performance than the conventional algorithms, while supporting finer-granular spatial scalability. [ABSTRACT FROM PUBLISHER]

In this letter, we provide a theoretical analysis of optimal predictive transform coding based on the Gaussian Markov random field (GMRF) model. It is shown that the eigen-analysis of the precision matrix of the GMRF model is optimal in decorrelating the signal. The resulting graph transform degenerates to the well-known 2-D discrete cosine transform (DCT) for a particular 2-D first order GMRF, although it is not a unique optimal solution. Furthermore, we present an optimal scheme to perform predictive transform coding based on conditional probabilities of a GMRF model. Such an analysis can be applied to both motion prediction and intra-frame predictive coding, and may lead to improvements in coding efficiency in the future. [ABSTRACT FROM AUTHOR]

Adaptive arithmetic coding is a general technique for coding source symbols of a stochastic process based on an adaptive model. The adaptive model provides measures of the statistics of source symbols and is updated, along with encoding/decoding processes, when more encoded/decoded symbols are fed as samples to the adaptive model. The coding performance depends on how well the adaptive model fits the statistics of source symbols. If the number of source symbols is large and the number of samples is small, the adaptive model may not be able to provide valid measures of the statistics, which results in an inefficient coding performance of the adaptive arithmetic coder. To this end, this paper presents segmentation of source symbols to improve the performance of the adaptive arithmetic coder. Each source symbol is divided into several segments. Each segment is separately coded with an adaptive arithmetic coder. With this division, possible values of each segment are concentrated within a small range. Given the limited number of samples, this concentration leads to a better fit of the adaptive model to the statistics of source symbols and therefore to an improvement of the coding efficiency. The proposed coding algorithm is applied to lossless motion vector coding for video transmission as an application example to show its performance improvement and coding gains. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a method for encoding continuous-time Gaussian signals subject to a usual data rate constraint and, more importantly, a reconstruction delay constraint. We first apply a Karhunen-Loève decomposition to reparameterize the continuous-time signal as a discrete sequence of vectors. We then study the optimal recursive quantization of this sequence of vectors. Since the optimal scheme turns out to have a very cumbersome design, we consider a simplified method, for which a numerical example suggests that the incurred performance loss is negligible. In this simplified method, we first build a state space model for the vector sequence and then use Bayessian tracking to sequentially encode each vector. The tracking task is performed using particle filtering. Numerical experiments show that the proposed approach offers visible advantages over other available approaches, especially when the reconstruction delay is small. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) systems create massive amounts of data which require huge resources for transmission or storage. The limited capacity of the downlink channel demands efficient onboard compression of SAR data. However, SAR raw data exhibit very little correlation which can be exploited in a compression algorithm. Range focusing is shown to increase the data correlation by exposing some of the distinctive features of the scene under surveillance. In this paper, we first present analysis of spotlight-mode SAR to show the source of the increased correlation in the range-focused data. Next, we propose two algorithms-transform-domain block predictive quantization (TD-BPQ) and transform-domain block predictive trellis-coded quantization (TD-BPTCQ)-for the compression of the range-focused data. Experimental results indicate that, at the rate of 1 bit/sample, and for similar or lower computational complexity, TD-BPQ and TD-BPTCQ outperform the best method proposed in the literature by 1.5 and 2.3 dB in signal-to-quantization-noise ratio, respectively. Similar improvements are observed for the rate of 2 bits/sample. [ABSTRACT FROM AUTHOR]