Share Email Print
cover

Proceedings Paper • new

Dynamic frame resizing with convolutional neural network for efficient video compression
Author(s): Jaehwan Kim; Youngo Park; Kwang Pyo Choi; JongSeok Lee; Sunyoung Jeon; JeongHoon Park
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

In the past, video codecs such as vc-1 and H.263 used a technique to encode reduced-resolution video and restore original resolution from the decoder for improvement of coding efficiency. The techniques of vc-1 and H.263 Annex Q are called dynamic frame resizing and reduced-resolution update mode, respectively. However, these techniques have not been widely used due to limited performance improvements that operate well only under specific conditions. In this paper, video frame resizing (reduced/restore) technique based on machine learning is proposed for improvement of coding efficiency. The proposed method features video of low resolution made by convolutional neural network (CNN) in encoder and reconstruction of original resolution using CNN in decoder. The proposed method shows improved subjective performance over all the high resolution videos which are dominantly consumed recently. In order to assess subjective quality of the proposed method, Video Multi-method Assessment Fusion (VMAF) which showed high reliability among many subjective measurement tools was used as subjective metric. Moreover, to assess general performance, diverse bitrates are tested. Experimental results showed that BD-rate based on VMAF was improved by about 51% compare to conventional HEVC. Especially, VMAF values were significantly improved in low bitrate. Also, when the method is subjectively tested, it had better subjective visual quality in similar bit rate.

Paper Details

Date Published: 19 September 2017
PDF: 13 pages
Proc. SPIE 10396, Applications of Digital Image Processing XL, 103961R (19 September 2017); doi: 10.1117/12.2270737
Show Author Affiliations
Jaehwan Kim, Samsung Electronics (Korea, Republic of)
Youngo Park, Samsung Electronics (Korea, Republic of)
Kwang Pyo Choi, Samsung Electronics (Korea, Republic of)
JongSeok Lee, Samsung Electronics (Korea, Republic of)
Sunyoung Jeon, Samsung Electronics (Korea, Republic of)
JeongHoon Park, Samsung Electronics (Korea, Republic of)


Published in SPIE Proceedings Vol. 10396:
Applications of Digital Image Processing XL
Andrew G. Tescher, Editor(s)

© SPIE. Terms of Use
Back to Top