14th International Conference on Quality of Multimedia Experience (QoMEX)
September 5-7, 2022 | Lippstadt, Germany
[PDF][Poster]
Hadi Amirpour (Alpen-Adria-Universität Klagenfurt), Raimund Schatz (AIT Austrian Institute of Technology, Austria), and Christian Timmerer (Alpen-Adria-Universität Klagenfurt)
Abstract:
We currently witness the rapidly growing importance of intelligent video streaming quality 
optimization and reduction of video delivery costs. Per-Title encoding, in contrast to a fixed bitrate ladder, shows significant promise to deliver higher quality video streams by addressing the trade-off between compression efficiency and video characteristics such as resolution and frame rate.
Selecting encodings with noticeable quality differences in between prevents the construction of an inefficient bitrate ladder that suffers from too similar quality representations.
In this respect, the VMAF metric represents a promising foundation for bitrate laddering, as it currently yields the highest video quality prediction performance. However, the minimum noticeable quality difference, referred as to just-noticeable-difference (JND), has not been properly validated for VMAF yet, with existing sources proposing highly diverse ΔVMAF step sizes ranging from two to six.
Abstract: In general, manipulated videos will eventually undergo recompression. Video transcoding will occur when the standard of recompression is different from the prior standard. Therefore, as a special sign of recompression, video transcoding can also be considered evidence of forgery in video forensics. In this paper, we focus on the detection and localization of video transcoding from AVC to HEVC (AVC-HEVC). There are two probable cases of AVC-HEVC transcoding – whole video transcoding and partial frame transcoding. However, the existing forensic methods only consider the detection of whole video transcoding, and they do not consider partial frame transcoding localization. In view of this, we propose a framewise scheme based on a convolutional neural network. First, we analyze that the essential difference between AVC-HEVC and HEVC is reflected in the high-frequency components of decoded frames. Then, 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. Finally, taking the decoded frames and PU maps as inputs, a dual-path network including specific convolutional modules and an adaptive fusion module is proposed. Through it, the artifacts on a single frame can be better extracted, and the transcoded frames can be detected and localized. Coupled with a simple voting strategy, the results of whole transcoding detection can be easily obtained. A large number of experiments are conducted to verify the performances. The results show that the proposed scheme outperforms or rivals the state-of-the-art methods in AVC-HEVC transcoding detection and localization.
Hadi Amirpour is a postdoc research fellow at ATHENA directed by Prof. Christian Timmerer. He received his B.Sc. degrees in Electrical and Biomedical Engineering, and he pursued his M.Sc. in Electrical Engineering. He got his Ph.D. in computer science from the University of Klagenfurt in 2022. He was appointed co-chair of Task Force 7 (TF7) Immersive Media Experience (IMEx) at the 15th Qualinet meeting. He was involved in the project EmergIMG, a Portuguese consortium on emerging imaging technologies, funded by the Portuguese funding agency and H2020. Currently, he is working on the ATHENA project in cooperation with its industry partner Bitmovin. His research interests are image processing and compression, video processing and compression, quality of experience, emerging 3D imaging technology, and medical image analysis.
