Subjective Portrait Region Cropping On Landscape Video Study

Cheng-Han Lee, Maniratnam Mandal, Neil Birkbeck, Yilin Wang, Balu Adsumilli, Alan C. Bovik

Abstract: With the rise of mobile video consumption, adapting videos to non-traditional aspect ratios poses challenges for existing content. The use of static cropping and border padding often compromises visual quality, while warping may distort a video’s intended meaning. Here we advocate for a more effective approach - cropping significant regions within video frames in a temporal manner, while minimizing distortion and preserving essential content. However, the lack of a large-scale database devoted to informing these tasks impedes progress in this direction. Addressing this gap, we introduce the LIVE-YouTube Video Cropping (LIVE-YT VC) database, featuring 1800 videos labeled by 90 human subjects. Sourced from the YouTube-UGC and LSVQ databases, this collection serves as the largest subjective video portrait region cropping database. We evaluate our methodology using the SmartVidCrop [1] algorithm, establishing a benchmark for future research. Our contributions offer a crucial resource for advancing video aspect ratio transformation, ensuring that mobile-friendly video content retains its quality and meaning. The details of accessing the dataset have been provided in the supplementary material.