  Encoding textural content remains a challenge for current standardised video
codecs. It is therefore beneficial to understand video textures in terms of
both their spatio-temporal characteristics and their encoding statistics in
order to optimize encoding performance. In this paper, we analyse the
spatio-temporal features and statistics of video textures, explore the
rate-quality performance of different texture types and investigate models to
mathematically describe them. For all considered theoretical models, we employ
machine-learning regression to predict the rate-quality curves based solely on
selected spatio-temporal features extracted from uncompressed content. All
experiments were performed on homogeneous video textures to ensure validity of
the observations. The results of the regression indicate that using an
exponential model we can more accurately predict the expected rate-quality
curve (with a mean Bj{\o}ntegaard Delta rate of 0.46% over the considered
dataset) while maintaining a low relative complexity. This is expected to be
adopted by in the loop processes for faster encoding decisions such as
rate-distortion optimisation, adaptive quantization, partitioning, etc.
