Machine learning-based fast transcoding for low-power video communication in Internet of Things

Aiming at the low-power video communication with low latency at resource-constrained terminals in the Internet of Things, a machine learning-based fast transcoding from distributed video coding (DVC) to high-efficiency video coding (HEVC) is put forward. In order to accelerate the transcoding, the D...

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Bibliographic Details
Published in:Signal, image and video processing image and video processing, 2022-11, Vol.16 (8), p.2183-2191
Main Authors: Yang, Jia, He, Pi-Yan, Yang, Hong, Xue, Ya-Juan, Jin, Yan-Hua
Format: Article
Language:English
Subjects:
Algorithms
Classifiers
Coding
Complexity
Computer Imaging
Computer Science
Decoding
Feature extraction
Image Processing and Computer Vision
Internet of Things
Machine learning
Multimedia Information Systems
Original Paper
Pattern Recognition and Graphics
Signal,Image and Speech Processing
Support vector machines
Video communication
Video compression
Vision
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Summary:Aiming at the low-power video communication with low latency at resource-constrained terminals in the Internet of Things, a machine learning-based fast transcoding from distributed video coding (DVC) to high-efficiency video coding (HEVC) is put forward. In order to accelerate the transcoding, the DVC decoding information is efficiently exploited to reduce HEVC encoding complexity at both levels of coding unit (CU) and prediction unit (PU). First of all, the predictions of CU partition and PU modes are regarded as two binary classification tasks. Subsequently, the initial features are extracted from the DVC decoding information and the support vector machine (SVM)-recursive feature elimination algorithm is adopted to select feature vectors to construct the training data which is used to train the SVM classifiers for CU and PU, respectively. By means of the top-down division prediction method, the CU partition is first determined by the trained SVM classifier. For the CUs which are not further split, the PU modes will be predicted to terminate the quad-tree coding process of HEVC in advance, so that HEVC encoding complexity is reduced. Experimental results show that the proposed algorithm can reduce 57.64% computational complexity on average with Bjøntegaard delta bit-rate 2.43%. In terms of transcoding time efficiency, the proposed algorithm outperforms the state-of-the-art fast DVC to HEVC transcoding algorithms based on machine learning.
ISSN:1863-1703
1863-1711
DOI:10.1007/s11760-022-02182-7