Abstract
Compressing video sequences with different content complexity results in
different bitrates for the same quality level or in different quality
levels for the same bitrate; for instance it is well known that content
with high spatial complexity and/or high motion requires high bitrates
for compression with adequate quality. To address this, per-title
optimization is used recently (e.g., by Netflix) to generate appropriate
rate-quality representations for different Video on demand (VoD) content
to be streamed via adaptive video streaming. However, this cannot be
adopted for live video streaming as it requires encoding (multiple
times) each video content. Spatial Information (SI) and Temporal
Information (TI) have been often used as indicators of video complexity,
for instance for preparing and describing content for video quality
assessment tests, and for rate-distortion modeling. However, it has been
questioned recently if different metrics could lead to a better
estimation of ”compressibility” of video. In this paper we compare
existing and proposed metrics in terms of their ability to estimate
”compressibility”. This supports quality-rate estimation and the
possibility to create appropriate ”quality ladders” (different quality
representations) for adaptive live video streaming. We observe in
particular that metrics related to the variance of pixel values provide
a good estimation of compressiblity for the considered datasets.