RNN-based bitstream feature extraction method for codec classification

Seungwoo Wee; Jechang Jeong

doi:10.1117/12.2521425

22 March 2019 RNN-based bitstream feature extraction method for codec classification

Seungwoo Wee, Jechang Jeong

Proceedings Volume 11049, International Workshop on Advanced Image Technology (IWAIT) 2019; 110493N (2019) https://doi.org/10.1117/12.2521425
Event: 2019 Joint International Workshop on Advanced Image Technology (IWAIT) and International Forum on Medical Imaging in Asia (IFMIA), 2019, Singapore, Singapore

Abstract

In this paper, we propose codec classification algorithm based on recurrent neural network (RNN) model. In video compression, codecs, such as MPEG2 and H.264/AVC, have their own distinctive data structure. These unique structures which are almost shown in header can be considered their feature. The proposed algorithm exploits that characteristics for classifying unknown bitstreams into specific codec. According to the fact that RNN is appropriate to time series data for learning to classification/recognition, the feature of an encoded bitstream can be extracted. We constitute the encoded bitstream as an input and give the bitstream its label indicating codec index. Two standard codecs, MPEG2 and H.264/AVC, are used in experiment. Experimental results show that the proposed RNN model classified bitstreams into corresponding codecs to some extent.

Citation Download Citation

Seungwoo Wee and Jechang Jeong "RNN-based bitstream feature extraction method for codec classification", Proc. SPIE 11049, International Workshop on Advanced Image Technology (IWAIT) 2019, 110493N (22 March 2019); https://doi.org/10.1117/12.2521425

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available