@InProceedings{Menon2021a, author = {Vignesh V Menon and Hadi Amirpour and Mohammad Ghanbari and Christian Timmerer}, booktitle = {2021 IEEE International Conference on Image Processing (ICIP)}, title = {{Efficient Content-Adaptive Feature-Based Shot Detection for HTTP Adaptive Streaming}}, year = {2021}, month = {sep}, pages = {2174--2178}, publisher = {IEEE}, abstract = {Video delivery over the Internet has been becoming a commodity in recent years, owing to the widespread use of Dynamic Adaptive Streaming over HTTP (DASH). The DASH specification defines a hierarchical data model for Media Presentation Descriptions (MPDs) in terms of segments. This paper focuses on segmenting video into multiple shots for encoding in Video on Demand (VoD) HTTP Adaptive Streaming (HAS) applications. Therefore, we propose a novel Discrete Cosine Transform (DCT) feature-based shot detection and successive elimination algorithm for shot detection and compare it against the default shot detection algorithm of the x265 implementation of the High Efficiency Video Coding (HEVC) standard. Our experimental results demonstrate that our proposed feature-based pre-processor has a recall rate of 25% and an F-measure of 20% greater than the benchmark algorithm for shot detection.}, doi = {10.1109/icip42928.2021.9506092}, keywords = {HTTP Adaptive Streaming, Video-on-Demand, Shot detection, multi-shot encoding}, url = {https://ieeexplore.ieee.org/document/9506092} }