@InProceedings{Amirpour2021a, author = {Hadi Amirpour and Hannaneh Barahouei Pasandi and Christian Timmerer and Mohammad Ghanbari}, booktitle = {2021 International Conference on Visual Communications and Image Processing (VCIP)}, title = {{Improving Per-title Encoding for HTTP Adaptive Streaming by Utilizing Video Super-resolution}}, year = {2021}, month = {dec}, pages = {1--5}, publisher = {IEEE}, abstract = {In per-title encoding, to optimize a bitrate ladder over spatial resolution, each video segment is downscaled to a set of spatial resolutions, and they are all encoded at a given set of bitrates. To find the highest quality resolution for each bitrate, the low-resolution encoded videos are upscaled to the original resolution, and a convex hull is formed based on the scaled qualities. Deep learning-based video super-resolution (VSR) approaches show a significant gain over traditional upscaling approaches, and they are becoming more and more efficient over time. This paper improves the per-title encoding over the upscaling methods by using deep neural network-based VSR algorithms. Utilizing a VSR algorithm by improving the quality of low-resolution encodings can improve the convex hull. As a result, it will lead to an improved bitrate ladder. To avoid bandwidth wastage at perceptually lossless bitrates, a maximum threshold for the quality is set, and encodings beyond it are eliminated from the bitrate ladder. Similarly, a minimum threshold is set to avoid low-quality video delivery. The encodings between the maximum and minimum thresholds are selected based on one Just Noticeable Difference. Our experimental results show that the proposed per-title encoding results in a 24% bitrate reduction and 53% storage reduction compared to the state-of-the-art method.}, doi = {10.1109/vcip53242.2021.9675403}, keywords = {Image coding, Visual communication, Bit rate, Superresolution, Bandwidth, Streaming media, Spatial resolution, HAS, per-title, deep learning, compression, bitrate ladder}, url = {https://ieeexplore.ieee.org/document/9675403} }