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Providing high-quality video with efficient bitrate is a main challenge invideo industry. The traditional one-size-fits-all scheme for bitrate ladders isinefficient and reaching the best content-aware decision computationallyimpractical due to extensive encodings required. To mitigate this, we propose abitrate and complexity efficient bitrate ladder prediction method usingtransfer learning and spatio-temporal features. We propose: (1) using featuremaps from well-known pre-trained DNNs to predict rate-quality behavior withlimited training data; and (2) improving highest quality rung efficiency bypredicting minimum bitrate for top quality and using it for the top rung. Themethod tested on 102 video scenes demonstrates 94.1% reduction in complexityversus brute-force at 1.71% BD-Rate expense. Additionally, transfer learningwas thoroughly studied through four networks and ablation studies.

Efficient Bitrate Ladder Construction using Transfer Learning and Spatio-Temporal Features