  In an adaptive bitrate streaming application, the efficiency of video
compression and the encoded video quality depend on both the video codec and
the quality metric used to perform encoding optimization. The development of
such a quality metric need large scale subjective datasets. In this work we
merge several datasets into one to support the creation of a metric tailored
for video compression and scaling. We proposed a set of HEVC lightweight
features to boost performance of the metrics. Our metrics can be computed from
tightly coupled encoding process with 4% compute overhead or from the decoding
process in real-time. The proposed method can achieve better correlation than
VMAF and P.1204.3. It can extrapolate to different dynamic ranges, and is
suitable for real-time video quality metrics delivery in the bitstream. The
performance is verified by in-distribution and cross-dataset tests. This work
paves the way for adaptive client-side heuristics, real-time segment
optimization, dynamic bitrate capping, and quality-dependent post-processing
neural network switching, etc.
