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检索条件"主题词=predictive coding"

共 1804 条记录，以下是1641-1650 订阅

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EDGE ADAPTIVE GRAPH-BASED TRANSFORMS: COMPARISON OF STEP/RAMP EDGE MODELS FOR VIDEO COMPRESSION

EDGE ADAPTIVE GRAPH-BASED TRANSFORMS: COMPARISON OF STEP/RAM...

引用

IEEE International Conference on Image Processing

作者： Yung-Hsuan Chao Hilmi E. Egilmez Antonio Ortega Sehoon Yea Bumshik Lee University of Southern California LG Electronics Inc.

ISBN: (纸本)9781467399623

In this paper, we propose a new edge model for edge adaptive graph-based transforms (EA-GBTs) in video compression. In particular, we consider step and ramp edge models to design graphs used for defining transforms, and compare their performance on coding intra and inter predicted residual blocks. In order to reduce the signaling overhead of block-adaptive coding, a new edge coding method is introduced for the ramp model. Our experimental results show that the proposed methods outperform classical DCT-based encoding and that ramp edge models provide better performance than step edge models for intra predicted residuals.

关键词： Graph-based transform Edge models Edge adaptive transforms Video compression predictive coding Video compression prediction coding edges telecommunication standards cellular radio Edge of a figure or solid Ramps RAMP coding Videotapes 3G mobile communication

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Segmentation based coding algorithm for low bit-rate video

Segmentation based coding algorithm for low bit-rate video

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： E. Salari Sheng Lin Department of Electrical Engineering University of Tolctlo and Eötvös Lárand University Toledo OH USA

A new motion compensated predictive coding based on object region segmentation is proposed for image sequence coding at low bit-rates. The motion compensated prediction involves segmentation, motion detection, and motion estimation for moving objects. Segmentation is carried out on the reconstructed images in both the encoder and decoder. This will eliminate the need to transmit the region shape information. Also, motion vector prediction is performed in both the encoder and decoder leading to a significant reduction of overhead for motion information. Motion compensated prediction errors are transformed using the discrete cosine transform (DCT) and the coefficients are quantized and entropy coded as recommended by the CCITT. Computer simulation shows that the proposed coding algorithm significantly reduces the block artifact which is a dominant distortion associated with the conventional block matching algorithms at low bit-rates.

关键词： Image segmentation Decoding Discrete cosine transforms predictive coding Image sequences Image coding Motion detection Motion estimation Image reconstruction Shape

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A novel DPCM algorithm using a nonlinear operator

A novel DPCM algorithm using a nonlinear operator

引用

IEEE International Conference on Image Processing

作者： Tian-Hu Yu S.K. Mitra Department of Information Engineering Chinese University of Hong Kong Sha Tin Hong Kong China Department of Electrical Rr Computer Engineering University of California Santa Barbara CA USA

Differential pulse-code modulation (DPCM) has found many applications in both intraframe and interframe coding of images. To improve its performance, several adaptive techniques have been introduced, but they make the resulting DPCM systems very complicated. In this work, we introduce a nonlinear scheme of predictive coding to exploiting spatial redundancy and amplitude redundancy of images based on the characteristics of the human visual system. The proposed scheme consists of a novel nonlinear algorithm to generate new information and a new compandor model of uniform quantization.< >

关键词： predictive coding Image coding Redundancy Delta modulation Pulse modulation Decoding Dynamic range Modulation coding Application software Humans

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The lossless DCT for combined lossy/lossless image coding

The lossless DCT for combined lossy/lossless image coding

引用

IEEE International Conference on Image Processing

作者： W. Philips Department of Telecommunications and Information Processing University of Gent IMEC Ghent Belgium

This paper introduces a modification of the discrete cosine transform (DCT) that produces integer coefficients from which the original image data can be reconstructed losslessly. It describes an embedded coding scheme which incorporates this lossless DCT and presents some experimental rate-distortion curves for this scheme. The results show that the lossless compression ratio of the proposed scheme exceeds that of the lossless JPEG predictive coding scheme. On the other hand, in lossy operation the rate-distortion curve of the proposed scheme is very close to that of lossy JPEG. Also, the transform coefficients of the proposed scheme can be decoded with the ordinary DCT at the expense of a small error, which is only significant in lossless operation.

关键词： Discrete cosine transforms Image coding Rate-distortion Transform coding Image reconstruction predictive coding Decoding

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Neurophysiological Correlates of Probabilistic Reward-Based Learning: Using Decoding Approach on MEG Data

Neurophysiological Correlates of Probabilistic Reward-Based ...

引用

Neurotechnologies and Neurointerfaces (CNN), International Conference

作者： Marina Ivanova Grigoriy Kopytin Viktoria Moiseeva Anna Shestakova Institute for Cognitive Neuroscience National Research University Higher School of Economics Moscow Russia

ISBN: (数字)9798350390926

ISBN: (纸本)9798350390933

Prediction error and volatility estimate are important concepts in the predictive coding theory. In the present study, we derive the values of prediction error and volatility estimate from a hierarchical Bayesian model - Hierarchical Gaussian Filter. Using support vector machine (SVM) method, we predict the values of prediction error and volatility estimate from brain activity measured by magnetoencephalography (MEG). Our findings suggest that these computational values are indeed represented in the neural data, supporting the neural basis of predictive coding mechanisms.

关键词： Support vector machines Magnetic separation Measurement uncertainty Magnetoencephalography predictive coding predictive models Probabilistic logic Filtering theory Vectors Neurotechnology

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A three-dimensional weighted median predictor for television

A three-dimensional weighted median predictor for television

引用

IEEE International Conference on Systems Engineering

作者： X. Song Y. Neuvo Signal Processing Laboratory Tampere University of Technology Tampere Finland

A novel nonlinear predictor based on weighted median filters is presented. The prediction is a median of three weighted median predictors. Experimental results show the validity of the weighted median predictors. Upon comparing the performance of the weighted median predictors with the nonlinear predictors and the linear predictor, the authors found that the weighted median predictors perform best.< >

关键词： TV Encoding Signal processing Laboratories Filters Accuracy Ear predictive coding Image reconstruction Quantization

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The use of data compression in telecomunication of Nuclear Medicine images

The use of data compression in telecomunication of Nuclear M...

引用

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Stuart A. Jackson Ivan Szasz Department of Nuclear Medicine Vancouver General Hospital University of British Columbia Vancouver Canada

Unlike conventional radiographs, the detail contained within Nuclear Medicine images is not fine in its structure. Consequently, it is possible that some loss of detail may be tolerable when using data compression techniques in image telecommunication. This study has been performed to compare a software implementation of the linear predictive coding method with a hardware based 2 dimensional cosine transform (2DCT). Using 50 normal bone scans with induced defects of varying significance, an experienced observer viewed the data in random order and receiver operating characteristic curves (ROC) were constructed to compare the responses.

关键词： Bones Observers Software Image coding Nuclear medicine Communications technology predictive coding

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The effectiveness of self-supervised representation learning in zero-resource subword modeling

The effectiveness of self-supervised representation learning...

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Asilomar Conference on Signals, Systems & Computers

作者： Siyuan Feng Odette Scharenborg Multimedia Computing Group Delft University of Technology Delft the Netherlands

For a language with no transcribed speech available (the zero-resource scenario), conventional acoustic modeling algorithms are not applicable. Recently, zero-resource acoustic modeling has gained much interest. One research problem is unsupervised subword modeling (USM), i.e., learning a feature representation that can distinguish subword units and is robust to speaker variation. Previous studies showed that self-supervised learning (SSL) has the potential to separate speaker and phonetic information in speech in an unsupervised manner, which is highly desired in USM. This paper compares two representative SSL algorithms, namely, contrastive predictive coding (CPC) and autoregressive predictive coding (APC), as a front-end method of a recently proposed, state-of-the art two-stage approach, to learn a representation as input to a back-end cross-lingual DNN. Experiments show that the bottleneck features extracted by the back-end achieved state of the art in a subword ABX task on the Libri-light and ZeroSpeech databases. In general, CPC is more effective than APC as the front-end in our approach, which is independent of the choice of the out-domain language identity in the back-end cross-lingual DNN and the training data amount. With very limited training data, APC is found similar or more effective than CPC when test data consists of long utterances.

关键词： Training Representation learning Databases Training data predictive coding predictive models Feature extraction

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OPTIMAL DELAYED DEcoding OF predictiveLY ENCODED SOURCES

OPTIMAL DELAYED DECODING OF PREDICTIVELY ENCODED SOURCES

引用

IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Vinay Melkote Kenneth Rose Department of Electrical and Computer Engineering University of California Santa Barbara CA 93106

ISBN: (纸本)9781424442959

predictive coding eliminates redundancy due to correlations between the current and past signal samples, so that only the innovation, or prediction residual, needs to be encoded. However, the decoder may, in principle, also exploit correlations with future samples. Prior decoder enhancement work mainly applied a non-causal filter to smooth the regular decoder reconstruction. In this work we broaden the scope to pose the problem: Given an allowed decoding delay, what is the optimal decoding algorithm for predictively encoded sources? To exploit all information available to the decoder, the proposed algorithm recursively estimates conditional probability densities, given both past and available future information, and computes the optimal reconstruction via conditional expectation. We further derive a near-optimal low complexity approximation to the optimal decoder, which employs a time-invariant lookup table or codebook approach. Simulations indicate that the latter method closely approximates the optimal delayed decoder, and that both considerably outperform the competition.

关键词： predictive coding smoothing delayed decoding DPCM recursive estimate prediction coding differential pulse code modulation decoders smoothing DEcoding conditional expectation

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Real-Time Human Body Sensors Data Classification using Supervised Machine Learning

Real-Time Human Body Sensors Data Classification using Super...

引用

Business Analytics for Technology and Security (ICBATS), International Conference on

作者： Muhammad Sajid Farooq Muhammad Farhan Khan Dept. of Computer Science Lahore Garrison University Lahore Pakistan Applied Science Research Center Applied Science Private University Amman Jordan Dept. of Forensic Sciences University of Health Sciences Lahore Pakistan

Supervised learning algorithms have recently been brought to the classification of medical data in an effort to boost diagnostic throughput and eliminate human-caused diagnostic errors. This measure was taken to improve diagnostic precision. To be carried out, the fully supervised learning process needs a large amount of accurately labelled data. Annotating data material, on the other hand, demands a large expenditure of time, effort, and resources. Using the self- supervised learning approach to solve this challenge has the potential to provide a variety of beneficial outcomes for everyone. When there is no direct supervision, pretext projects are regularly employed in independent study scenarios. This is done so that massive volumes of unsupervised data may be utilized to obtain supervised knowledge. Furthermore, by utilizing specialized, supervised information, the network is prepared to obtain representations that will be beneficial for still-growing professions. The two-step CPC-based classification framework (TCC) model is built completely using contrastive predictive coding and is divided into two distinct phases that are carried out in sequential order. Making use of the contrastive predictive coding (CPC) model, the initial step is to build a fictional position. It must be completed as quickly as possible. This activity must utilize the encoder to extract appropriate features from a broad variety of classes in order to achieve the goal that it has set out to achieve. The encoder will employ the skills they learned while working on the pretext in the second half, which will take place in a training setting under the guidance of an expert teacher. Finally, we assess the proposed framework by comparing it to the most recent and innovative research in the field. This allows us to highlight the framework’s success by displaying how well it compares to other cutting-edge and current industry efforts. Experiments will be conducted to compare the efficacy of the pr

关键词： Training Adaptation models Supervised learning Machine learning predictive coding predictive models Feature extraction

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