Christian Timmerer

TL;DR: Wang et al. as mentioned in this paper proposed a novel light field image compression method that enables viewport scalability, quality scalability and spatial scalability while keeping compression efficiency high, which can adapt to the display type, transmission channel, network condition, processing power, and user needs.

TL;DR: In this paper , the authors presented ECAS-ML, Edge Assisted Adaptation Scheme for HTTP Adaptive Streaming with Machine Learning, which focuses on managing the tradeoff among bitrate, segment switches, and stalls to achieve a higher quality of experience (QoE).

TL;DR: Days of Future Past+ is introduced, a heuristic algorithm that takes advantage of the features of the latest HTTP version, HTTP/3, to provide high Quality of Experience (QoE) to the viewers and examines different strategies of download order for those segments to optimize the QoE in limited resources scenarios.

TL;DR: Experimental results show that, on average, OPSEyields bitrate savings of up to 48.88% in certain scenes to maintain the same VMAF, compared to the reference HTTP Live Streaming (HLS) bitrate ladder without any noticeable additional latency in streaming.

TL;DR: Wang et al. as mentioned in this paper proposed a framewise scheme based on a convolutional neural network for the detection and localization of video transcoding from AVC to HEVC (AVC-HEVC), where the partition and location information of prediction units (PUs) are introduced to generate frame-level PU maps to make full use of the local artifacts of PUs.