Multi-resolution extreme learning machine-based side information estimation in distributed video coding

Dash, Bodhisattva; Rup, Suvendu; Mohapatra, Anjali; Majhi, Banshidhar; Swamy, M.N.S.

doi:10.1007/s11042-018-5921-9

Cited by 3 publications

(2 citation statements)

References 44 publications

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“…For traditional video compression algorithms, a lot of neural network based methods have been proposed for particular modules such as intra prediction and residual coding [23], entropy coding [24] in order to improve the performance of system. For distributed video coding, several deep learning based SI generation methods [25,26] have been proposed. Authors in [25] use a deep belief network with four 16 × 16 key frame blocks as the input blocks to predict the side information.…”

Section: Introductionmentioning

confidence: 99%

“…Authors in [25] use a deep belief network with four 16 × 16 key frame blocks as the input blocks to predict the side information. In [26], extreme learning machine neural network is used to estimate transformed coefficients of the WZ frame. These proposed SI generation schemes have obtained improvements in terms of both qualitative and quantitative measures.…”

Section: Introductionmentioning

confidence: 99%

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Improving TDWZ Correlation Noise Estimation: A Deep Learning based Approach

Tien

Huong

Van

et al. 2020

REV J. Electron. Commun.

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Transform domain Wyner-Ziv video coding (TDWZ) has shown its benefits in compressing video applications with limited resources such as visual surveillance systems, remote sensing and wireless sensor networks. In TDWZ, the correlation noise model (CNM) plays a vital role since it directly affects to the number of bits needed to send from the encoder and thus the overall TDWZ compression performance. To achieve CNM with high accurate for TDWZ, we propose in this paper a novel CNM estimation approach in which the CNM with Laplacian distribution is adaptively estimated based on a deep learning (DL) mechanism. The proposed DL based CNM includes two hidden layers and a linear activation function to adaptively update the Laplacian parameter. Experimental results showed that the proposed TDWZ codec significantly outperforms the relevant benchmarks, notably by around 35% bitrate saving when compared to the DISCOVER codec and around 22% bitrate saving when compared to the HEVC Intra benchmark while providing a similar perceptual quality.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improving TDWZ Correlation Noise Estimation: A Deep Learning based Approach

Tien

Huong

Van

et al. 2020

REV J. Electron. Commun.

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Phase-based side information generation in distributed video coding

Chen

Wang

et al. 2022

Multimed Tools Appl

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Stacked Denoising Extreme Learning Machine Autoencoder Based on Graph Embedding for Feature Representation

Sun

Zhao

et al. 2019

IEEE Access

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Extreme learning machine is characterized by less training parameters, fast training speed, and strong generalization ability. It has been applied to obtain feature representations from the complex data in the tasks of data clustering or classification. In this paper, a graph embedding-based denoising extreme learning machine autoencoder (GDELM-AE) is proposed for capturing the structure of the inputs. Specifically, in GDELM-AE, a graph embedding framework that contains an intrinsic graph and a penalty graph constructed by local Fisher discrimination analysis is integrated into the autoencoder. So, it can exploit both local structure and global structure information in extreme learning machine (ELM) spaces. Further, we propose a stacked graph embedded denoising (SGD)-ELM by stacking several GDELM-AEs. The experimental results on several benchmarks validate that GDELM-AE can obtain efficient and robust feature representation of original data; moreover, the stacked GDELM-AE can obtain high-level and noiserobust representations. The comparative results with the state-of-the-art algorithms indicate that the proposed algorithm can obtain better accuracy as well as faster training speed. INDEX TERMS Extreme learning machine, stacked autoencoder, denoising, graph embedding.

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Multi-resolution extreme learning machine-based side information estimation in distributed video coding

Cited by 3 publications

References 44 publications

Improving TDWZ Correlation Noise Estimation: A Deep Learning based Approach

Improving TDWZ Correlation Noise Estimation: A Deep Learning based Approach

Phase-based side information generation in distributed video coding

Stacked Denoising Extreme Learning Machine Autoencoder Based on Graph Embedding for Feature Representation

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