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ADAPTIVE predictive coding FOR NEWSPAPER FACSIMILE

PROCEEDINGS OF THE IEEE 1980年第7期68卷 807-813页

作者： USUBUCHI, T OMACHI, T IINUMA, K Central Research Laboratories Nippon Electric Company Limited Kawasaki Japan

An adaptive predictive coding method, which efficiently encodes newspaper pages with printed text and screened photographs is presented. This coding technique utilizes two kinds of predictors with different reference picture elements (pels). One is applied to printed text and the other is applied to screened photographs. The Dth previous pel and its neighboring pels age adopted as the reference pels for the photograph predictor, where distance D coincides with the screen period. Comparing the adaptive predictive coding with a typical document facsimile coding, the data compression ratio is improved by about two times for a screened photograph (compression ratio is 5 ∼ 6) and is almost the same or slightly higher for printed text. Computer simulation shows that if a 500-kbits buffer memory is employed, it is possible to transmit most pages, including an extreme case of a 100 percent photograph page, with a 4500-rev/min scanner at a transmission bit rate of 128 kbit/s. For average pages the revolution speed can be raised to 6000 rev/min. Page transmission time of about 5 min in analog facsimile through a 48-kHz band can be reduced to 1.8 min by adopting the digital transmission with a 128-kbit/s data modem and the adaptive predictive coding technique, when the facsimile revolution speed is set at 6000 rev/min.

关键词： predictive coding Facsimile Data compression Decoding Computer simulation Bit rate Modems Application software Statistics

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How can a recurrent neurodynamic predictive coding model cope with fluctuation in temporal patterns? Robotic experiments on imitative interaction

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NEURAL NETWORKS 2017年 92卷 3-16页

作者： Ahmadi, Ahmadreza Tani, Jun Korea Adv Inst Sci & Technol Cognit Neurorobot Lab Dept Elect Engn N1 Bldg291 Daehak Ro373-1 Guseong Dong Daejeon 305701 South Korea Grad Univ Okinawa Inst Sci & Technol Cognit Neurorobot Res Unit 1919-1 Tancha Onnason Okinawa 9040495 Japan

The current paper examines how a recurrent neural network (RNN) model using a dynamic predictive coding scheme can cope with fluctuations in temporal patterns through generalization in learning. The conjecture driving this present inquiry is that a RNN model with multiple timescales (MTRNN) learns by extracting patterns of change from observed temporal patterns, developing an internal dynamic structure such that variance in initial internal states account for modulations in corresponding observed patterns. We trained a MTRNN with low-dimensional temporal patterns, and assessed performance on an imitation task employing these patterns. Analysis reveals that imitating fluctuated patterns consists in inferring optimal internal states by error regression. The model was then tested through humanoid robotic experiments requiring imitative interaction with human subjects. Results show that spontaneous and lively interaction can be achieved as the model successfully copes with fluctuations naturally occurring in human movement patterns. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Neuro-robotics predictive coding Recurrent neural networks Synchronized imitation Time-warping Error regression

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Lateral predictive coding revisited:internal model,symmetry breaking,and response time

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Communications in Theoretical Physics 2022年第9期74卷 158-169页

作者： Zhen-Ye Huang Xin-Yi Fan Jianwen Zhou Hai-Jun Zhou CAS Key Laboratory for Theoretical Physics Institute of Theoretical PhysicsChinese Academy of SciencesBeijing 100190China School of Physical Sciences University of Chinese Academy of SciencesBeijing 100049China MinJiang Collaborative Center for Theoretical Physics MinJiang UniversityFuzhou 350108China

predictive coding is a promising theoretical framework in neuroscience for understanding information transmission and *** posits that the brain perceives the external world through internal models and updates these models under the guidance of prediction *** studies on predictive coding emphasized top-down feedback interactions in hierarchical multilayered networks but largely ignored lateral recurrent *** perform analytical and numerical investigations in this work on the effects of single-layer lateral *** consider a simple predictive response dynamics and run it on the MNIST dataset of hand-written *** find that learning will generally break the interaction symmetry between peer neurons,and that high input correlation between two neurons does not necessarily bring strong direct interactions between *** optimized network responds to familiar input signals much faster than to novel or random inputs,and it significantly reduces the correlations between the output states of pairs of neurons.

关键词： neural network response dynamics predictive coding similarity symmetry breaking

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On ordering color maps for lossless predictive coding

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IEEE TRANSACTIONS ON IMAGE PROCESSING 1996年第11期5卷 1522-1527页

作者： Memon, ND Venkateswaran, A COMP POWER GRP OAK BROOKIL 60521

Linear predictive techniques perform poorly when used with color-mapped images where pixel values represent indices that point to color values in a look-up table, Reordering the color table, however, can lead to a lower entropy of prediction errors, In this paper, we investigate the problem of ordering the color table such that the absolute sum of prediction errors is minimized, The problem turns out to be intractable, even for the simple case of one-dimensional (1-D) prediction schemes, We give two heuristic solutions for the problem and use them for ordering the color table prior to encoding the image by lossless predictive techniques. We demonstrate that significant improvements in actual bit rates can be achieved over dictionary-based coding schemes that are commonly employed for color-mapped images.

关键词： predictive coding Image coding Pixel Table lookup Entropy predictive models Color Image storage Bit rate Data compression

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A single-spike model of predictive coding

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NEUROCOMPUTING 2000年 32卷 17-23页

作者： Ballard, DH Rao, RPN Zhang, ZH Univ Rochester Dept Comp Sci Rochester NY 14627 USA Salk Inst Biol Studies CNL La Jolla CA 92037 USA

The standard cortical model assumes that the meaning of a neuron's signal is contained in its firing rate. While that model has been used to interpret a voluminous amount of experimental data, it does not address the question of timing, or how recipient neurons can decode this signal in time to predict behavioral results. We propose a model based on coincident firing of large groups of neurons. We show using an example of predictive coding, how the cortex can support vast amounts of non-interfering parallel computation. (C) 2000 Published by Elsevier Science B.V. All rights reserved.

关键词： neural coding model predictive coding synchronous firing

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A single spike model of predictive coding

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NEUROCOMPUTING 2004年 58卷 165-171页

作者： Zhang, ZH Ballard, DH Univ Rochester Dept Comp Sci Rochester NY 14627 USA

Current models of cortical computation are based on analog quantities instead of single spikes. This paper extends the predictive coding model (Nature Neurosci. 2(1) (1999) 79) to the level of neural signaling. Neurons in our model use a mixed strategy to transmit information. Spikes are not only messages of computation, but also carriers of information with analog quantities encoded in their phases. Computation is shared among cells both in time and in space, such that information is signaled probabilistically in a distributed synchronous fashion. Contrary to "noise other than signal" interpretation of irregularity of neural signaling, our model proposes a computational role of such variability. (C) 2004 Published by Elsevier B.V.

关键词： distributed synchrony predictive coding probabilistic firing sparse coding minimum description length

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Optimal Reference Selection for Random Access in predictive coding Schemes

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IEEE TRANSACTIONS ON COMMUNICATIONS 2020年第9期68卷 5819-5833页

作者： Pham, Mai-Quyen Roumy, Aline Maugey, Thomas Dupraz, Elsa Kieffer, Michel INRIA IRISA Team Project SIROCCO F-35042 Rennes France CNRS UMR 6285 IMT Atlantique Lab STICC Technopole Brest IroiseCS 83818 F-29238 Brest France Univ Paris Saclay Cent Supelec L2S CNRSUMR 8506 F-91192 Gif Sur Yvette France

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.

关键词： Encoding Video coding predictive coding Videos Data models Indexes Standards predictive coding random access integer linear programming

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A new lossy compression algorithm for wireless sensor networks using Bayesian predictive coding

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WIRELESS NETWORKS 2020年第8期26卷 5981-5995页

作者： Chen, Chen Zhang, Limao Tiong, Robert Lee Kong Nanyang Technol Univ Sch Civil & Environm Engn 50 Nanyang Ave Singapore 639798 Singapore

Wireless sensor networks (WSNs) generate a variety of continuous data streams. To reduce data storage and transmission cost, compression is recommended to be applied to the data streams from every single sensor node. Local compression falls into two categories: lossless and lossy. Lossy compression techniques are generally preferable for sensors in commercial nodes than the lossless ones as they provide a better compression ratio at a lower computational cost. However, the traditional approaches for data compression in WSNs are sensitive to sensor accuracy. They are less efficient when there are abnormal and faulty measurements or missing data. This paper proposes a new lossy compression approach using the Bayesian predictive coding (BPC). Instead of the original signals, predictive coding transmits the error terms which are calculated by subtracting the predicted signals from the actual signals to the receiving node. Its compression performance depends on the accuracy of the adopted prediction technique. BPC combines the Bayesian inference with the predictive coding. Prediction is made by the Bayesian inference instead of regression models as in traditional predictive coding. In this way, it can utilize prior information and provide inferences that are conditional on the data without reliance on asymptotic approximation. Experimental tests show that the BPC is the same efficient as the linear predictive coding when handling independent signals which follow a stationary probability distribution. More than that, the BPC is more robust toward occasionally erroneous or missing sensor data. The proposed approach is based on the physical knowledge of the phenomenon in applications. It can be considered as a complementary approach to the existing lossy compression family for WSNs.

关键词： Bayesian inference Lossy compression predictive coding Wireless sensor networks

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Recurrent auto-encoder with multi-resolution ensemble and predictive coding for multivariate time-series anomaly detection

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APPLIED INTELLIGENCE 2023年第21期53卷 25330-25342页

作者： Choi, Heejeong Kim, Subin Kang, Pilsung Korea Univ Sch Ind & Management Engn 145Anam Ro Seoul South Korea

As large-scale time-series data can easily be found in real-world applications, multivariate time-series anomaly detection has played an essential role in diverse industries. It enables productivity improvement and maintenance cost reduction by preventing malfunctions and detecting anomalies based on time-series data. However, multivariate time-series anomaly detection is challenging because real-world time-series data exhibit complex temporal dependencies. For this task, it is crucial to learn a rich representation that effectively contains the nonlinear temporal dynamics of normal behavior. In this study, we propose an unsupervised multivariate time-series anomaly detection model named RAE-MEPC which learns informative normal representations based on multi-resolution ensemble reconstruction and predictive coding. We introduce multi-resolution ensemble encoding to capture the multi-scale dependency from the input time series. The encoder hierarchically aggregates the multi-scale temporal features extracted from the sub-encoders with different encoding lengths. From these encoded features, the reconstruction decoder reconstructs the input time series based on multi-resolution ensemble decoding where lower-resolution information helps to decode sub-decoders with higher-resolution outputs. predictive coding is further introduced to encourage the model to learn more temporal dependencies of the time series. Experiments on real-world benchmark datasets show that the proposed model outperforms the benchmark models for multivariate time-series anomaly detection.

关键词： Time-series anomaly detection Recurrent auto-encoder Multi-resolution ensemble predictive coding

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Pel adaptive predictive coding based on image segmentation for lossless compression

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 1999年第6期E82A卷 1037-1046页

作者： Nakachi, T Fujii, T Suzuki, J NTT Network Innovat Labs Yokosuka Kanagawa 2390847 Japan

In this paper, we propose an adaptive predictive coding method based on image segmentation for lossless compression. MAR (Multiplicative Autoregressive) predictive coding is an efficient lossless compression scheme. Predictors of the MAR model can be adapted to changes in the local image statistics due to its local image processing. However, the performance of the MAR method is reduced when applied to images whose local statistics change within the block-by-block subdivided image. Furthermore, side-information such as prediction coefficients must be transmitted to the decoder with each block. In order to enhance the compression performance, we improve the MAR coding method by using image segmentation. The proposed MAR predictor can be adapted to the local statistics of the image efficiently at each pixel. Furthermore, less side-information need be transmitted compared with the conventional MAR method.

关键词： lossless coding predictive coding super high definition images image segmentation

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