In this paper, we propose to employ predictive coding for lossy compression of synthetic aperture radar (SAR) raw data. We exploit the known result that a blockwise normalized SAR raw signal is a Gaussian stationary p...
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In this paper, we propose to employ predictive coding for lossy compression of synthetic aperture radar (SAR) raw data. We exploit the known result that a blockwise normalized SAR raw signal is a Gaussian stationary process in order to design an optimal decorrelator for, this signal. We show that, due to the statistical properties of the SAR signa, 1, an along-range linear predictor With few, taps is able to effectively capture most of the raw signal correlation. The proposed predictive coding algorithm, which performs. quantization of the prediction error, optionally followed by entropy coding, exhibits a number of advantages, and notably an interesting performance/complexity trade-off, with respect to other techniques such as flexible block adaptive quantization (FBAQ) or methods. based on transform-coding;fractional output bit-rates can, also be achieved in the entropy-constrained mode. Simulation results on real-world SIR-C/X-SAR as well as simulated raw and image data show that the proposed algorithm outperforms FBAQ as to SNR, at a computational cost compatible with modern SAR systems.
This paper presents a complete general-purpose method for still-image compression called adaptive prediction trees. Efficient lossy and lossless compression of photographs, graphics, textual, and mixed images is achie...
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This paper presents a complete general-purpose method for still-image compression called adaptive prediction trees. Efficient lossy and lossless compression of photographs, graphics, textual, and mixed images is achieved by ordering the data in a multicomponent binary pyramid, applying an empirically optimized nonlinear predictor, exploiting structural redundancies between color components, then coding with hex-trees and adaptive runlength/Huffman coders. Color palettization and order statistics prefiltering are applied adaptively as appropriate. Over a diverse image test set, the method outperforms standard lossless and lossy alternatives. The competing lossy alternatives use block transforms and wavelets in well-studied configurations. A major result of this paper is that predictive coding is a viable and sometimes preferable alternative to these methods.
Individuals with normal vision can sometimes momentarily mistake one object for another. In this functional magnetic resonance imaging study, we investigated how extrastriate visual regions respond during these errone...
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Individuals with normal vision can sometimes momentarily mistake one object for another. In this functional magnetic resonance imaging study, we investigated how extrastriate visual regions respond during these erroneous perceptual judgements. Subjects were asked to discriminate images of houses and faces that were degraded such that they were close to an individually defined threshold for perception. On correct trials, voxels localized on the inferior occipital (OFA), fusiform (FFA) and parahippocampal (PPA) gyri exhibited selectivity for face and house images as expected. On incorrect trials, no face- or place-selectivity was observed for OFA or PPA. However, consistent with 'predictive coding' accounts of perception, we observed that the FFA also responded robustly on trials where a house was misperceived as a face, and concurrent activation was observed in medio-frontal and right parietal regions previously implicated in decision making under uncertainty. We suggest that FFA responses during misperception may be driven by a predictive top-down signal from these regions.
This paper addresses the problem of robust communication of predictively encoded video in a joint source-channel setting. Specifically, the problem of predictive mismatch, where there is a drift between the state of t...
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ISBN:
(纸本)0769518966
This paper addresses the problem of robust communication of predictively encoded video in a joint source-channel setting. Specifically, the problem of predictive mismatch, where there is a drift between the state of the encoder and the decoder is addressed as a variant of the Wyner-Ziv problem. We propose a video encoding algorithm based on the H.26L video codec which prevents the propagation of error in predictively encoded video in the event of predictive mismatch (or drift) between the encoder and the decoder. One of the main advantages of the proposed approach is that there is minimal loss in performance over the standard H.26L encoder during error-free transmission, while simultaneously allowing error recovery in the event of errors. Using turbo codes as coset codes, we evaluate the performance of the proposed codec and demonstrate the efficacy of the proposed framework. The performance of the proposed approach can only improve with the use of superior coset codes.
Large scale scientific simulation codes typically run on a cluster of CPUs that write/read time steps to/from a single file system. As data sets are constantly growing in size, this increasingly leads to I/O bottlenec...
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Large scale scientific simulation codes typically run on a cluster of CPUs that write/read time steps to/from a single file system. As data sets are constantly growing in size, this increasingly leads to I/O bottlenecks. When the rate at which data is produced exceeds the available I/O bandwidth, the simulation stalls and the CPUs are idle. Data compression can alleviate this problem by using some CPU cycles to reduce the amount of data needed to be transfered. Most compression schemes, however, are designed to operate offline and seek to maximize compression, not throughput. Furthermore, they often require quantizing floating-point values onto a uniform integer grid, which disqualifies their use in applications where exact values must be retained. We propose a simple scheme for lossless, online compression of floating-point data that transparently integrates into the I/O of many applications. A plug-in scheme for data-dependent prediction makes our scheme applicable to a wide variety of data used in visualization, such as unstructured meshes, point sets, images, and voxel grids. We achieve state-of-the-art compression rates and speeds, the latter in part due to an improved entropy coder. We demonstrate that this significantly accelerates I/O throughput in real simulation runs. Unlike previous schemes, our method also adapts well to variable-precision floating-point and integer data.
We present a novel lossless compression algorithm which compresses sequences of three-dimensional (3D) volumes collected during a functional magnetic resonance imaging (fMRI) experiment. The large data sets involved i...
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ISBN:
(纸本)9781424404810
We present a novel lossless compression algorithm which compresses sequences of three-dimensional (3D) volumes collected during a functional magnetic resonance imaging (fMRI) experiment. The large data sets involved in this popular biomedical application necessitate fast and efficient compression methods. We propose to use 3D prediction, temporal decorrelation and entropy coding with context modeling for encoding the fMRI scans after preprocessing with the region-of-interest (ROI) masking. The proposed algorithm is conceptually simple and can achieve fast implementation and efficient coding performance. We illustrate computer simulations to show advantages over conventional coding methods.
This paper presents a software-only, real-time video coder/decoder (codec) for use with low-bandwidth channels where the bandwidth is unknown or varies with time. The codec incorporates a modified JPEG2000 core and in...
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ISBN:
(纸本)0819463027
This paper presents a software-only, real-time video coder/decoder (codec) for use with low-bandwidth channels where the bandwidth is unknown or varies with time. The codec incorporates a modified JPEG2000 core and interframe predictive coding, and can operate with network bandwidths of less than 1 kbits/second. The encoder and decoder establish two virtual connections over a single IP-based communications link. The first connection is UDP/IP guaranteed throughput, which is used to transmit the compressed video stream in real time, while the second is TCP/IP guaranteed delivery, which is used for two-way control and compression parameter updating. The TCP/IP link serves as a virtual feedback channel and enables the decoder to instruct the encoder to throttle back the transmission bit rate in response to the measured packet loss ratio. It also enables either side to initiate on-the-fly parameter updates such as bit rate, frame rate, frame size, and correlation parameter, among others. The codec also incorporates frame-rate throttling whereby the number of frames decoded is adjusted based upon the available processing resources. Thus, the proposed codec is capable of automatically adjusting the transmission bit rate and decoding frame rate to adapt to any network scenario. Video coding results for a variety of network bandwidths and configurations are presented to illustrate the vast capabilities of the proposed video coding system.
Large scale scientific simulation codes typically run on a cluster of CPUs that write/read time steps to/from a single file system. As data sets are constantly growing in size, this increasingly leads to I/O bottlenec...
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Large scale scientific simulation codes typically run on a cluster of CPUs that write/read time steps to/from a single file system. As data sets are constantly growing in size, this increasingly leads to I/O bottlenecks. When the rate at which data is produced exceeds the available I/O bandwidth, the simulation stalls and the CPUs are idle. Data compression can alleviate this problem by using some CPU cycles to reduce the amount of data needed to be transfered. Most compression schemes, however, are designed to operate offline and seek to maximize compression, not throughput. Furthermore, they often require quantizing floating-point values onto a uniform integer grid, which disqualifies their use in applications where exact values must be retained. We propose a simple scheme for lossless, online compression of floating-point data that transparently integrates into the I/O of many applications. A plug-in scheme for data-dependent prediction makes our scheme applicable to a wide variety of data used in visualization, such as unstructured meshes, point sets, images, and voxel grids. We achieve state-of-the-art compression rates and speeds, the latter in part due to an improved entropy coder. We demonstrate that this significantly accelerates I/O throughput in real simulation runs. Unlike previous schemes, our method also adapts well to variable-precision floating-point and integer data.
Image sequence prediction is widely used in image compression and transmission schemes such as differential pulse code modulation (DPCM). In traditional predictive coding, linear predictors are usually adopted to expl...
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ISBN:
(纸本)9781424404810
Image sequence prediction is widely used in image compression and transmission schemes such as differential pulse code modulation (DPCM). In traditional predictive coding, linear predictors are usually adopted to exploit the inherent redundancy and correlation between neighboring pixels. However, due to the nonstationary and non-Gaussian nature of image sequences, linear predictors are not often very effective. As an alternative, Volterra predictor is able to compensate for the smoothing effects introduced by linear predictor. However, it suffers from noise that may be attributed to quantization errors or image acquisition devices. In this paper, we propose a novel nonlinear polynomial weighted median (PWM) predictor for image sequence prediction. The proposed PWM predictor is more robust to noise, while still retaining the information of higher-order statistics of pixel values. Experimental results illustrate that the PWM predictor yields better results than other predictors especially in noisy case. The proposed scheme can be incorporated in new predictive coding systems.
The Free Energy Principle (FEP) has been proposed as a unifying explanation of adaptive behaviour within self-organising systems subsuming perception, action, and cognition under one computational umbrella. Within neu...
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The Free Energy Principle (FEP) has been proposed as a unifying explanation of adaptive behaviour within self-organising systems subsuming perception, action, and cognition under one computational umbrella. Within neuroscience, the FEP serves as the mathematical and normative foundation for two influential models: predictive coding and active inference, which together generate testable predictions across the aforementioned domains. Still, with explanatory ambitions as grand as the FEP's there remain many areas that are either empirically under explored, or are too theoretically under developed to be explored empirically. As such this thesis has two complementary aims. The first aim is to test a set of the predictions of the classic formulation of predictive coding by examining the temporal effects of prediction error at multiple levels of the visual hierarchy using time-resolved multivariate pattern analysis and electroencephalography. The second aim is primarily theoretical. As it stands it is not clear how, under active inference or predictive coding, perceptual information transitions from unconscious processing, where it is inaccessible from the first person point of view, to conscious processing where it is available for subjective report. To help bridge this gap, we will present a computational model of conscious access based on the partially observable Markov decision process formulation of active inference. Through simulation we demonstrate the model's ability to: i) reproduce a wide range of electrophysiological and behavioural results; ii) reconcile/explain (apparently) conflicting findings; and iii), generate novel predictions that can be tested empirically. In doing so we aim to expand the explanatory scope of the FEP to experimental paradigms commonly used in consciousness science.
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