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robust coding over noisy overcomplete channels

IEEE TRANSACTIONS ON IMAGE PROCESSING 2007年第2期16卷 442-452页

作者： Doi, Eizaburo Balcan, Doru C. Lewicki, Michael S. Carnegie Mellon Univ Ctr Neural Basis Cognit Pittsburgh PA 15213 USA Carnegie Mellon Univ Dept Comp Sci Pittsburgh PA 15213 USA

We address the problem of robust coding in which the signal information should be preserved in spite of intrinsic noise in the representation. We present a theoretical analysis for 1- and 2-D cases and characterize the optimal linear encoder and decoder in the mean-squared error sense. Our analysis allows for an arbitrary number of coding units, thus including both under- and over-complete representations, and provides insights into optimal coding strategies. In particular, we show how the form of the code adapts to the number of coding units and to different data and noise conditions in order to achieve robustness. We also present numerical solutions of robust coding for high-dimensional image data, demonstrating that these codes are substantially more robust than other linear image coding methods such as PCA, ICA, and wavelets.

关键词： channel capacity constraint channel noise mean-squared error (MSE) bounds overcomplete representations robust coding

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robust coding for Lossy Computing with Receiver-Side Observation Costs

Robust Coding for Lossy Computing with Receiver-Side Observa...

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IEEE International Symposium on Information Theory (ISIT)

作者： Ahmadi, Behzad Simeone, Osvaldo New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA

ISBN: (纸本)9781457705953

(1)An encoder wishes to minimize the bit rate necessary to guarantee that a decoder is able to calculate a symbol-wise function of a sequence available only at the encoder and a sequence that can be measured only at the decoder. here by including two new aspects: (i) The decoder obtains noisy measurements of its sequence, where the quality of such measurements can be controlled via a cost-constrained "action" sequence;(ii) Measurement at the decoder may fail in a way that is unpredictable to the encoder, thus requiring robust encoding. The considered scenario generalizes known settings such as the Heegard-Berger-Kaspi and the "source coding with a vending machine" problems. The rate-distortion-cost function is derived in relevant special cases, along with general upper and lower bounds. Numerical examples are also worked out to obtain further insight into the optimal system design.

关键词： Decoding Distortion measurement Irrigation Loss measurement Noise measurement robustness Zinc bit rate minimization cost-constrained action sequence decoder decoding encoder encoding lossy computing noisy measurement rate distortion cost function rate distortion theory receiver-side observation cost receivers robust coding sequences

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Structured regularized robust coding for face recognition

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NEUROCOMPUTING 2016年 216卷 18-27页

作者： Wang, Xing Yang, Meng Shen, Linlin Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou Guangdong Peoples R China

The sparse representation based classifier (SRC) has been successfully applied to robust face recognition (FR) with various variations. To achieve much stronger robustness to facial occlusion, regularized robust coding (RRC) was proposed by designing a new robust representation residual term. Although RRC has achieved the leading performance, it ignores the structured information (i.e., spatial consistence) embedded in the occluded pixels. In this paper, we proposed a novel structured regularized robust coding (SRRC) framework, in which a weight value is assigned to each pixel to measure its importance in the coding procedure and the spatial consistence of occluded pixels is exploited by the pixel weight learning (PWL) model. Efficient algorithms were also proposed to fast learn each pixel's weight value. The experiments on face recognition in several representative datasets clearly show the advantage of the proposed SRRC in accuracy and efficiency. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Structured regularized robust coding Sparse representation Pixel weight learning Face recognition

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Adaptive locality-constrained regularized robust coding for image recognition

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OPTIK 2019年 178卷 1208-1217页

作者： Du, Haishun Ma, Luogang Hu, Qingpu Wang, Sheng Zhang, Yanyu Henan Univ Inst Image Proc & Pattern Recognit Kaifeng 475004 Peoples R China

We propose a novel adaptive locality-constrained regularized robust coding (ALRRC) for image recognition by considering both the effects of the test sample's features and the local relationships between the test sample and the training samples in the linear coding procedure. By adaptively calculating out a feature weight matrix of a test sample, ALRRC measures the effects of the test sample's features in the linear coding procedure. Furthermore, ALRRC can obtain the weighted test sample by multiplying the test sample by the feature weight matrix, which has reduced the roles of the aberrant features as far as possible. Similarly, ALRRC can get the weighted training samples by multiplying the training samples by the feature weight matrix. Moreover, using the similarities between the weighted test sample and weighted training samples, ALRRC adaptively calculates out a locality-constrained matrix that can truly characterize the local relationships between the test sample and the training samples. Finally, by incorporating the feature weight matrix and locality-constrained matrix into the linear coding framework, ALRRC is inclined to select the true local training samples to represent the test sample. In addition, we also propose an iterative algorithm to solve the optimization problem of ALRRC. Experimental results on several image databases show that ALRRC is more effective and robust than state-of-the-art linear coding-based methods.

关键词： Locality constraint Feature weight robust coding Linear coding Image recognition

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Regularized robust coding for Face Recognition

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2013年第5期22卷 1753-1766页

作者： Yang, Meng Zhang, Lei Yang, Jian Zhang, David Hong Kong Polytech Univ Dept Comp Hong Kong Hong Kong Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Technol Nanjing 210094 Jiangsu Peoples R China

Recently the sparse representation based classification (SRC) has been proposed for robust face recognition (FR). In SRC, the testing image is coded as a sparse linear combination of the training samples, and the representation fidelity is measured by the l(2)-norm or l(1)-norm of the coding residual. Such a sparse coding model assumes that the coding residual follows Gaussian or Laplacian distribution, which may not be effective enough to describe the coding residual in practical FR systems. Meanwhile, the sparsity constraint on the coding coefficients makes the computational cost of SRC very high. In this paper, we propose a new face coding model, namely regularized robust coding (RRC), which could robustly regress a given signal with regularized regression coefficients. By assuming that the coding residual and the coding coefficient are respectively independent and identically distributed, the RRC seeks for a maximum a posterior solution of the coding problem. An iteratively reweighted regularized robust coding (I (RC)-C-3) algorithm is proposed to solve the RRC model efficiently. Extensive experiments on representative face databases demonstrate that the RRC is much more effective and efficient than state-of-the-art sparse representation based methods in dealing with face occlusion, corruption, lighting, and expression changes, etc.

关键词： Face recognition regularization robust coding sparse representation

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Structured Regularized robust coding for Face Recognition 1

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1st Chinese Conference on Computer Vision (CCCV)

作者： Yang, Meng Song, Tiancheng Liu, Feng Shen, Linlin Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China

ISBN: (数字)9783662485705

ISBN: (纸本)9783662485705;9783662485699

The sparse representation based classifier (SRC) has been successfully applied to robust face recognition (FR) with various variations. To achieve much stronger robustness to facial occlusion, recently regularized robust coding (RRC) was proposed by designing a new robust representation residual term. Although RRC has achieved the leading performance, it ignores the structured information (i.e., spatial consistence) embedded in the occluded pixels. In this paper, we proposed a novel structured regularized robust coding (SRRC) framework, in which the spatial consistence of occluded pixels was exploited by pixel weight learning (PWL) model. Efficient algorithms were also proposed to fastly learn the pixel's weight and accurately recover the occluded area. The experiments on face recognition in several representative datasets clearly show the advantage of the proposed SRRC in accuracy and efficiency.

关键词： Structure regularized robust coding Face recognition

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A Dynamic, Economical, and robust coding Scheme in the Lateral Prefrontal Neurons of Monkeys 29th

A Dynamic, Economical, and Robust Coding Scheme in the Later...

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29th International Conference on Neural Information Processing

作者： Sakamoto, Kazuhiro Saito, Naohiro Yoshida, Shun Mushiake, Hajime Tohoku Med & Pharmaceut Univ Fac Med Dept Neurosci 1-15-1 FukumuroMiyagino Ku Sendai Miyagi 9838536 Japan Tohoku Univ Dept Physiol Sch Med 2-1 Seiryo MachiAoba Ku Sendai Miyagi 9808575 Japan

ISBN: (纸本)9783031301070;9783031301087

The lateral prefrontal cortex (lPFC) plays crucial roles in executive functions, including working memory and behavioral planning. The functions of lPFC require conservation of its limited neuronal resources. Herein, we examined lPFC neuronal activities in monkeys during a path-planning task that required behavioral planning and working memory. We analyzed the coding dynamics of final-goal neurons, and found selective and sustained activities toward the final goal, reflecting working memory. Putative excitatory pyramidal neurons shifted their scheme from discrete to collective coding during the preparatory period of the task, whereas inhibitory interneurons used a collective coding scheme.

关键词： lateral prefrontal cortex monkey dynamic coding economical coding robust coding

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A Two-Stage Approach for robust HEVC coding and Streaming

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2018年第8期28卷 1960-1973页

作者： Carreira, Joao F. M. Assuncao, Pedro A. de Faria, Sdrgio M. M. Ekmekcioglu, Erhan Kondoz, Ahmet Loughborough Univ London Inst Digital Technol London E15 2GZ England Inst Telecomunicacoes P-2411901 Leiria Portugal Inst Politecn Leiria P-2411901 Leiria Portugal

The increased compression ratios achieved by the High Efficiency Video coding (HEVC) standard lead to reduced robustness of coded streams, with increased susceptibility to network errors and consequent video quality degradation. This paper proposes a method based on a two-stage approach to improve the error robustness of HEVC streaming, by reducing temporal error propagation in the case of frame loss. The prediction mismatch that occurs at the decoder after frame loss is reduced through the following two stages. First, at the encoding stage, the reference pictures are dynamically selected based on constraining conditions and Lagrangian optimization, which distributes the use of reference pictures, by reducing the number of prediction units that depend on a single reference. Second, at the streaming stage, a motion vector (MV) prioritization algorithm, based on spatial dependencies, selects an optimal subset of MVs to be transmitted, redundantly, as side information to reduce mismatched MV predictions at the decoder. The simulation results show that the proposed method significantly reduces the effect of temporal error propagation. Compared with the reference HEVC, the proposed reference picture selection method is able to improve the video quality at low-packet-loss rates (e.g., 1%) using the same bitrate, achieving quality gains up to 2.3 dB for 10% of packet loss ratio. It is shown, for instance, that the redundant MVs are able to boost the performance achieving quality gains of 3 dB when compared with the reference HEVC, at the cost using 4% increase in total bitrate.

关键词： HEVC robust coding reference picture selection redundant MV error propagation

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robust perceptual coding of overcomplete frame expansions

Robust perceptual coding of overcomplete frame expansions

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Conference on Human Vision and Electronic Imaging VI

作者： Pinilla-Dutoit, J Woolley, SI Univ Birmingham Sch Elect & Elect Engn Birmingham B15 2TT W Midlands England

ISBN: (纸本)0819439770

The cortex transform provides a meaningful representation of images in terms of the responses of cortical cells. It is based on experimental results from human vision research. The multiple orientations obtained in the expansion are of interest for image analysis applications. In image coders, quantization can exploit to a large extent psychovisual properties. This transform belongs to a group of overcomplete transforms. This property has not benefited their use in coding applications. However, the inherent redundancy of overcomplete representations can be exploited to increase the robustness of the code. Multiple description coding of overcomplete expansions has been reported to confer more graceful degradation to partial reconstructions in the event of channel erasures. This paper proposes a coding strategy based on oriented transforms that yield perceptually meaningful coefficients. The coding budget is reduced by sampling and quantization. The remaining redundancy is used to provide robustness. In addition, the descriptions can be organized to allow progressive reconstruction. The tradeoff between quantization strength, perceived quality, redundancy and robustness can be incorporate in the design of the coder.

关键词： cortex transform wavelet transform steerable pyramid overcomplete frame expansion multiple description coding robust coding perceptual coding progressive coding image coding image compression

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robust face hallucination via locality-constrained multiscale coding

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INFORMATION SCIENCES 2020年 512卷 369-380页

作者： Li, Na Liu, Licheng Li, Shutao Lin, Hui Hunan Univ Dept Elect & Informat Engn Changsha 410082 Hunan Peoples R China Hunan Univ Key Lab Visual Percept & Artificial Intelligence Changsha 410082 Hunan Peoples R China

Face hallucination (FH) is to produce face images with High Resolution (HR) from Low Resolution (LR) observations. Unfortunately, most existing FH methods fail to make full use of the local geometrical information, especially when the LR images are corrupted by noise. Inspired by the observation that regions with large scales can provide much useful information, in this paper we propose a robust Locality-constrained Multiscale coding (RLMC) based method to forecast HR face images while suppressing noise and outliers. In RLMC, a weight vector is used in the loss function to ease the effect of outliers in data representation. Furthermore, inspired by the observation that abundant local information can be exploited by jointly representing overlapping patches with multiple scales. Simultaneously encoding multiple scale patches encourages different scales to share complementary information, which admits the proposed method to generate more appropriate coefficients for super-resolution reconstruction. Experimental results verified the effectiveness of the proposed method in terms of both quantitative measurements and visual impressions. (C) 2019 Published by Elsevier Inc.

关键词： Multiple representation Face hallucination Locality geometry robust coding

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