Yongjun Wu
Senior Principal Engineer – Prime Video
In this paper, we present a large-scale HDR video quality dataset for sports content that includes the above mentioned important issues in live streaming, and a method of merging multiple datasets using anchor videos.
Prime Video’s algorithm optimized encoding for secondary content and manifest generation for content stitching, achieving the best performance and compatibility on customer devices.
Prime Video invented a new way to set the producer reference time as the global time reference for live-event playback on customer devices.
Prime Video used closed-form equations and solutions to represent and compress audio media timelines in a pattern template, which ensures a second-order, lossless compression for audio stream media timelines.
During the Winter Conference on Applications of Computer Vision (WACV), Prime Video’s Yongjun Wu and Sriram Sethuraman discussed Video/Audio Quality in Computer Vision, and Hai Wei presented the HDR VQM Grand Challenge awards.
We propose a new prototype model for no-reference video quality assessment (VQA) based on the natural statistics of space-time chips of videos. Space-time chips (ST-chips) are a new, quality-aware feature space which we define as space-time localized cuts of video data in directions that are determined by the local motion flow.
We conducted the first large-scale study of medium and low-bitrate videos from live sports for two codecs (Elemental AVC and HEVC) and created the Amazon Prime Video Low-Bitrate Sports (APV LBS) dataset.
We present a new method and a large-scale database to detect audio-video synchronization (A/V sync) errors in tennis videos.
We built a video quality database specifically designed for live streaming VQA research. The new video database is called the Laboratory for Image and Video Engineering (LIVE) Live stream Database. The LIVE Livestream Database includes 315 videos of 45 contents impaired by 6 types of distortions.