  Bit depth adaptation, where the bit depth of a video sequence is reduced
before transmission and up-sampled during display, can potentially reduce data
rates with limited impact on perceptual quality. In this context, we conducted
a subjective study on a UHD video database, BVI-BD, to explore the relationship
between bit depth and visual quality. In this work, three bit depth adaptation
methods are investigated, including linear scaling, error diffusion, and a
novel adaptive Gaussian filtering approach. The results from a subjective
experiment indicate that above a critical bit depth, bit depth adaptation has
no significant impact on perceptual quality, while reducing the amount
information that is required to be transmitted. Below the critical bit depth,
advanced adaptation methods can be used to retain `good' visual quality (on
average) down to around 2 bits per color channel for the outlined experimental
setup - a large reduction compared to the typically used 8 bits per color
channel. A selection of image quality metrics were subsequently bench-marked on
the subjective data, and analysis indicates that a bespoke quality metric is
required for bit depth adaptation.
