  In HTTP Adaptive Streaming, video content is conventionally encoded by
adapting its spatial resolution and quantization level to best match the
prevailing network state and display characteristics. It is well known that the
traditional solution, of using a fixed bitrate ladder, does not result in the
highest quality of experience for the user. Hence, in this paper, we consider a
content-driven approach for estimating the bitrate ladder, based on
spatio-temporal features extracted from the uncompressed content. The method
implements a content-driven interpolation. It uses the extracted features to
train a machine learning model to infer the curvature points of the Rate-VMAF
curves in order to guide a set of initial encodings. We employ the VMAF quality
metric as a means of perceptually conditioning the estimation. When compared to
exhaustive encoding that produces the reference ladder, the estimated ladder is
composed by 74.3% of identical Rate-VMAF points with the reference ladder. The
proposed method offers a significant reduction of the number of encodes
required, 77.4%, at a small average Bj{\o}ntegaard Delta Rate cost, 1.12%.
