  In HTTP adaptive live streaming applications, video segments are encoded at a
fixed set of bitrate-resolution pairs known as bitrate ladder. Live encoders
use the fastest available encoding configuration, referred to as preset, to
ensure the minimum possible latency in video encoding. However, an optimized
preset and optimized number of CPU threads for each encoding instance may
result in (i) increased quality and (ii) efficient CPU utilization while
encoding. For low latency live encoders, the encoding speed is expected to be
more than or equal to the video framerate. To this light, this paper introduces
a Just Noticeable Difference (JND)-Aware Low latency Encoding Scheme (JALE),
which uses random forest-based models to jointly determine the optimized
encoder preset and thread count for each representation, based on video
complexity features, the target encoding speed, the total number of available
CPU threads, and the target encoder. Experimental results show that, on
average, JALE yield a quality improvement of 1.32 dB PSNR and 5.38 VMAF points
with the same bitrate, compared to the fastest preset encoding of the HTTP Live
Streaming (HLS) bitrate ladder using x265 HEVC open-source encoder with eight
CPU threads used for each representation. These enhancements are achieved while
maintaining the desired encoding speed. Furthermore, on average, JALE results
in an overall storage reduction of 72.70 %, a reduction in the total number of
CPU threads used by 63.83 %, and a 37.87 % reduction in the overall encoding
time, considering a JND of six VMAF points.
