Open AccessClassification of Compressed Domain Images Utilizing Open VINO Inference Engine
Author(s) -
Kevin Tan,
Boezura Borhanuddin,
Yee Wan Wong,
Thomas Wei Min Ooi,
J.G. Khor
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.a2709.109119
Subject(s) - discrete cosine transform , computer science , inference , convolutional neural network , artificial intelligence , jpeg , decoding methods , pattern recognition (psychology) , inference engine , computer vision , kernel (algebra) , transform coding , data compression , image (mathematics) , algorithm , mathematics , combinatorics
This paper provides a platform to investigate and explore method of ‘partial decoding of JPEG images’ for image classification using Convolutional Neural Network (CNN). The inference is targeting to run on computer system with x86 CPU architecture. We aimed to improve the inference speed of classification by just using part of the compressed domain image information for prediction. We will extract and use the ‘Discrete Cosine Transform’ (DCT) coefficients from compressed domain images to train our models. The trained models are then converted into OpenVINO Intermediate Representation (IR) format for optimization. During inference stage, full decoding is not required as our model only need DCT coefficients which are presented in the process of image partial decoding. Our customized DCT model are able to achieve up to 90% validation and testing accuracy with great competence towards the conventional RGB model. We can also obtain up to 2x times inference speed boost while performing inference on CPU in compressed domain compared with spatial domain employing OpenVINO inference engine.