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Hierarchical predictive coding-Based JND Estimation for Image Compression

IEEE TRANSACTIONS ON IMAGE PROCESSING 2021年 30卷 487-500页

作者： Wang, Hongkui Yu, Li Liang, Junhui Yin, Haibing Li, Tiansong Wang, Shengwei Huazhong Univ Sci & Technol Coll Elect Informat Engn Wuhan 430074 Peoples R China Hangzhou Dianzi Univ Sch Commun Engn Hangzhou 310018 Peoples R China

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.

关键词： Visualization Estimation Visual perception predictive coding Image coding predictive models Neurons JND estimation hierarchical predictive coding positive and negative incentives masking effects VVC

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New studies on adaptive predictive coding of images using multiplicative autoregressive models

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IEEE TRANSACTIONS ON IMAGE PROCESSING 1992年第1期1卷 106-111页

作者： Das, M. Loh, N. K. Oakland Univ Dept Elect & Syst Engn Rochester MI 48309 USA

This paper introduces two new one-dimensional multiplicative autoregressive (MAR) models for adaptive predictive coding of digitized images. The proposed scheme offers a number of advantages. These include easy implementability, a high signal-to-noise ratio at a moderate bit rate, and guaranteed stability of the predictive coder. Results of extensive experimental studies are presented.

关键词： predictive coding predictive models Stability Parameter estimation Image coding Pulse modulation Signal to noise ratio Bit rate Two dimensional displays Context modeling

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A Hierarchical predictive coding Model of Object Recognition in Natural Images

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COGNITIVE COMPUTATION 2017年第2期9卷 151-167页

作者： Spratling, M. W. Kings Coll London Dept Informat London WC2R 2LS England

predictive coding has been proposed as a model of the hierarchical perceptual inference process performed in the cortex. However, results demonstrating that predictive coding is capable of performing the complex inference required to recognise objects in natural images have not previously been presented. This article proposes a hierarchical neural network based on predictive coding for performing visual object recognition. This network is applied to the tasks of categorising hand-written digits, identifying faces, and locating cars in images of street scenes. It is shown that image recognition can be performed with tolerance to position, illumination, size, partial occlusion, and within-category variation. The current results, therefore, provide the first practical demonstration that predictive coding (at least the particular implementation of predictive coding used here;the PC/BC-DIM algorithm) is capable of performing accurate visual object recognition.

关键词： predictive coding Neural networks Object recognition Implicit shape model Deep neural networks Sparse coding

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An effective compression algorithm for real-time transmission data using predictive coding with mixed models of LSTM and XGBoost

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NEUROCOMPUTING 2021年 462卷 247-259页

作者： Yan, Zhidan Wang, Junfei Sheng, Li Yang, Zhenyu China Univ Petr East China Coll Control Sci & Engn Qingdao 266580 Shandong Peoples R China

In this paper, a compression and decompression algorithm is proposed for real-time transmission data by combining discrete wavelet decomposition and reconstruction, hybrid model prediction of long short -term memory (LSTM) and extreme gradient boosting (XGBoost), and the development of quantization sequences. The forward compression and reverse decompression are achieved by continuously acquiring the prediction error and data series with quantization error through the iterative shifting approach. To accurately forecast data series with quantization errors, three classes of data with specific distribution characteristics collected from the measurement-while-drilling (MWD) operation field are decomposed into approximate data and detailed data, which are then predicted by autoregressive integrated moving average (ARIMA), support vector regression (SVR), XGBoost, backpropagation neural network (BPNN), radial basis function neural network (RBFNN), and LSTM models. Particularly, the LSTM model and the XGBoost model are proven to be suitable strategies for working with approximate data and detailed data separately by comparing with conventional approaches in terms of the mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2) predictive indexes. Besides, experiment studies show that almost 50% compression performance can be reached under one in ten thousand dis-tortions. It is verified that the proposed algorithm is appropriate and efficient for real-time data transmis-sion applications with high performance. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Compression predictive coding Discrete wavelet transform LSTM XGBoost

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World models and predictive coding for cognitive and developmental robotics: frontiers and challenges

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ADVANCED ROBOTICS 2023年第13期37卷 780-806页

作者： Taniguchi, Tadahiro Murata, Shingo Suzuki, Masahiro Ognibene, Dimitri Lanillos, Pablo Ugur, Emre Jamone, Lorenzo Nakamura, Tomoaki Ciria, Alejandra Lara, Bruno Pezzulo, Giovanni Ritsumeikan Univ Dept Informat Sci & Engn Kyoto Japan Keio Univ Tokyo Japan Univ Tokyo Tokyo Japan Univ Milano Bicocca Milan Italy Univ Essex Colchester England Donders Inst Brain Cognit & Behav Nijmegen Netherlands Spanish Natl Res Council Cajal Int Neurosci Ctr Madrid Spain Bogazici Univ Istanbul Turkiye Queen Mary Univ London London England Univ Electrocommun Tokyo Japan Univ Nacl Autonoma Mexico Mexico City Mexico Univ Autonoma Estado Morelos Cuernavaca Morelos Mexico Natl Res Council Italy Inst Cognit Sci & Technol Rome Italy

Creating autonomous robots that can actively explore the environment, acquire knowledge and learn skills continuously is the ultimate achievement envisioned in cognitive and developmental robotics. Importantly, if the aim is to create robots that can continuously develop through interactions with their environment, their learning processes should be based on interactions with their physical and social world in the manner of human learning and cognitive development. Based on this context, in this paper, we focus on the two concepts of world models and predictive coding. Recently, world models have attracted renewed attention as a topic of considerable interest in artificial intelligence. Cognitive systems learn world models to better predict future sensory observations and optimize their policies, i.e. controllers. Alternatively, in neuroscience, predictive coding proposes that the brain continuously predicts its inputs and adapts to model its own dynamics and control behavior in its environment. Both ideas may be considered as underpinning the cognitive development of robots and humans capable of continual or lifelong learning. Although many studies have been conducted on predictive coding in cognitive robotics and neurorobotics, the relationship between world model-based approaches in AI and predictive coding in robotics has rarely been discussed. Therefore, in this paper, we clarify the definitions, relationships, and status of current research on these topics, as well as missing pieces of world models and predictive coding in conjunction with crucially related concepts such as the free-energy principle and active inference in the context of cognitive and developmental robotics. Furthermore, we outline the frontiers and challenges involved in world models and predictive coding toward the further integration of AI and robotics, as well as the creation of robots with real cognitive and developmental capabilities in the future.

关键词： World model cognitive robotics predictive coding free-energy principle active inference

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Enhancing the predictive coding efficiency with control technologies for lossless compression of images

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IET IMAGE PROCESSING 2012年第3期6卷 251-263页

作者： Lee, C. -H. Kau, L. -J. Natl Chung Hsin Univ Dept Mech Engn Taichung 402 Taiwan Natl Taipei Univ Technol Dept Elect Engn Taipei 10608 Taiwan Natl Taipei Univ Technol Grad Inst Comp & Commun Engn Taipei 10608 Taiwan

This study applies techniques commonly used in control systems to enhance the efficiency of predictive coding in lossless compression of images for pixels around boundaries. Actually, the predictive coding system behaves just like a multi-input single-output system with the predictor itself can be regarded as the system model. Besides, the prediction error is usually feedback for the adaptation of predictor coefficients so that the prediction error of consecutive pixels can be minimised. When compared with a control system, which is to follow the system command as precisely as possible, the authors find the objective of both systems are the same. Moreover, a boundary among image pixels can be considered a step command in control systems. These observations lead to the idea of using control technologies to improve the prediction result around boundaries. To realise this idea, an adaptive Takagi-Sugeno fuzzy neural network and a proportional controller in control systems are applied as the predictor and the error compensator, respectively. To accelerate the run-time performance of the proposed system under limited resources, the online training area is even not used for network adaptation, but the performance is still comparable with state-of-the-art predictors and coders as the authors will see in the experiment.

关键词： adaptive Takagi-Sugeno fuzzy neural network predictor Neural computing techniques error compensator control technology Computer vision and image processing techniques Image and video coding proportional control image coding multiinput single-output system predictive coding lossless image compression proportional controller neural nets

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Visually Guided Sound Source Separation With Audio-Visual predictive coding

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第11期35卷 15528-15542页

作者： Song, Zengjie Zhang, Zhaoxiang Xi An Jiao Tong Univ Sch Math & Stat Xian 710049 Peoples R China Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Ctr Res Intelligent Percept & Comp Beijing 100190 Peoples R China Chinese Acad Sci Hong Kong Inst Sci & Innovat Ctr Artificial Intelligence & Robot Hong Kong Peoples R China

The framework of visually guided sound source separation generally consists of three parts: visual feature extraction, multimodal feature fusion, and sound signal processing. An ongoing trend in this field has been to tailor involved visual feature extractor for informative visual guidance and separately devise module for feature fusion, while utilizing U-Net by default for sound analysis. However, such a divide-and-conquer paradigm is parameter-inefficient and, meanwhile, may obtain suboptimal performance as jointly optimizing and harmonizing various model components is challengeable. By contrast, this article presents a novel approach, dubbed audio-visual predictive coding (AVPC), to tackle this task in a parameter-efficient and more effective manner. The network of AVPC features a simple ResNet-based video analysis network for deriving semantic visual features, and a predictive coding (PC)-based sound separation network that can extract audio features, fuse multimodal information, and predict sound separation masks in the same architecture. By iteratively minimizing the prediction error between features, AVPC integrates audio and visual information recursively, leading to progressively improved performance. In addition, we develop a valid self-supervised learning strategy for AVPC via copredicting two audio-visual representations of the same sound source. Extensive evaluations demonstrate that AVPC outperforms several baselines in separating musical instrument sounds, while reducing the model size significantly. Code is available at: https://***/zjsong/Audio-Visual-predictive-coding.

关键词： Visualization Feature extraction Source separation Task analysis Semantics Self-supervised learning predictive coding Feature fusion multimodal learning predictive coding (PC) self-supervised learning sound source separation

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INTERFRAME predictive coding OF IMAGES USING HYBRID VECTOR QUANTIZATION

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IEEE TRANSACTIONS ON COMMUNICATIONS 1986年第4期34卷 411-415页

作者： BAGE, MJ INST NATL RECH SCI TELECOMMUN VERDUN H3E 1H6 QUEBEC CANADA BELL NO RES VERDUN H3E 1H6 QUEBEC CANADA

The temporal and spatial redundancies of image sequences can be reduced by prediction and vector quantization, respectively. The robustness of the coder is increased through the use of a hybrid two-stage vector quantizer. The first stage is of the Linde-Buzo-Gray type. The second stage encodes the residual error of the first stage using a lattice vector quantizer.

关键词： predictive coding Vector quantization Redundancy Robustness Distortion measurement Books Mean square error methods Video sequences Pulse modulation Entropy

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Lossless predictive coding of electric signal waveforms

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2017年第5期25卷 3540-3552页

作者： Duda, Krzysztof AGH Univ Sci & Technol Fac Elect Engn Automat Comp Sci & Biomed Engn Dept Measurement & Elect Krakow Poland

This paper describes a new predictive coding algorithm designed for lossless compression of electric signal waveforms. Prediction coefficients are obtained from the sinusoidal signal autoregressive model. Predictors for a single sinusoid and the sum of two and three sinusoids are considered. Integer-to-integer coding is obtained by the proper (i.e. reversible) quantization of computations. The proposed coding algorithm shows better performance than the commonly used 2nd order differences coding and linear prediction coding that is optimal in the least squares sense.

关键词： Electric power systems lossless signal compression predictive coding integer-to-integer coding

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Lossy predictive coding of SAR raw data

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2003年第5期41卷 977-987页

作者： Magli, E Olmo, G Politecn Torino Dipartimento Elettron I-10129 Turin Italy

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.

关键词： differential pulse code modulation (DPCM) entropy coding entropy-constrained quantization predictive coding synthetic aperture radar (SAR) raw data compression

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