Block Based Enhancement using Deep Learning for Conversion of Low
Resolution AVS Video to High Resolution HEVC Video
Abstract
Conversion of one video bitstream to another video bitstream is a
challenging task in the heterogeneous transcoder due to different video
formats. In this paper, a region of interest (ROI) based super
resolution technique is used to convert the lowresolution AVS (audio
video standard) video to high definition HEVC (high efficiency video
coding) video. Firstly, we classify a low-resolution video frame into
small blocks by using visual characteristics, transform coefficients,
and motion vector (MV) of a video. These blocks are further classified
as blocks of most interest (BOMI), blocks of less interest (BOLI) and
blocks of noninterest (BONI). The BONI blocks are considered as
background blocks due to less interest in video and remains unchanged
during SR process. Secondly, we apply deep learning based super
resolution method on low resolution BOMI, and BOLI blocks to enhance the
visual quality. The BOMI and BOLI blocks have high attention due to ROI
that include some motion and contrast of the objects. The proposed
method saves 20% to 30% computational time and obtained appreciable
results as compared with full frame based super resolution method. We
have tested our method on different official video sequences with
resolution of 1K, 2K, and 4K. Our proposed method has an efficient
visual performance in contrast to the full frame-based super resolution
method.