Dr. Honggui Li Profile

Dr. Honggui Li

at Yangzhou Univ

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SPIE Journal Paper | 26 September 2017

Deep linear autoencoder and patch clustering-based unified one-dimensional coding of image and video

Honggui Li

JEI, Vol. 26, Issue 05, 053016, (September 2017) https://doi.org/10.1117/12.10.1117/1.JEI.26.5.053016

KEYWORDS: Image compression, Video coding, Simulation of CCA and DLA aggregates, Video, Reconstruction algorithms, 3D image processing, Chromium, Lithium, 3D video compression, Video compression

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This paper proposes a unified one-dimensional (1-D) coding framework of image and video, which depends on deep learning neural network and image patch clustering. First, an improved K-means clustering algorithm for image patches is employed to obtain the compact inputs of deep artificial neural network. Second, for the purpose of best reconstructing original image patches, deep linear autoencoder (DLA), a linear version of the classical deep nonlinear autoencoder, is introduced to achieve the 1-D representation of image blocks. Under the circumstances of 1-D representation, DLA is capable of attaining zero reconstruction error, which is impossible for the classical nonlinear dimensionality reduction methods. Third, a unified 1-D coding infrastructure for image, intraframe, interframe, multiview video, three-dimensional (3-D) video, and multiview 3-D video is built by incorporating different categories of videos into the inputs of patch clustering algorithm. Finally, it is shown in the results of simulation experiments that the proposed methods can simultaneously gain higher compression ratio and peak signal-to-noise ratio than those of the state-of-the-art methods in the situation of low bitrate transmission.

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