2025 BEITC Proceedings

Two-Pass Encoding for Live Video Streaming - $15

 2025 NAB BEIT Conference Proceedings Best Student Paper Award Winner

Live streaming has become increasingly important in our daily lives due to the growing demand for real-time content consumption. Traditional live video streaming typically relies on single-pass encoding due to its low latency. However, it lacks video content analysis, often resulting in inefficient compression and quality fluctuations during playback. Constant Rate Factor (CRF) encoding, a type of single-pass method, offers more consistent quality but suffers from unpredictable output bitrate, complicating bandwidth management. In contrast, multi-pass encoding improves compression efficiency through multiple passes. However, its added latency makes it unsuitable for live streaming. In this paper, we propose OTPS, an online two-pass encoding scheme that overcomes these limitations by employing fast feature extraction on a downscaled video representation and a gradient-boosting regression model to predict the optimal CRF for encoding. This approach provides consistent quality and efficient encoding while avoiding the latency introduced by traditional multi-pass techniques. Experimental results show that OTPS offers 3.7% higher compression efficiency than single-pass encoding and achieves up to 28.1% faster encoding than multi-pass modes. Compared to single-pass encoding, encoded videos using OTPS exhibit 5% less deviation from the target bitrate while delivering notably more consistent quality.

Mohammad Ghasempour, Hadi Amirpour, Christian Timmerer | Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität | Klagenfurt, Austria

Verifying Video Signals Using Computer Vision and Machine Learning Techniques  - $15

High-quality video streams pass through various processes inside media workflows, including pre-processing, transcoding, editing, post-processing, etc. The media transformation in these processes can alter the properties of the intermediate video, potentially disrupting subsequent processes or degrading the final output video quality experienced by viewers. Reliable content delivery necessitates verification of video data to ensure an acceptable quality of experience (QoE). The verification of video data is not just limited to the measurement of degradation in perceived quality on the viewing screen but also considers validation of video parameters affecting the proper functioning of media devices. For example, the Y, Cb, and Cr values are altered after lossy compression that can result in out-of-gamut RGB data and hence will lead to erroneous functioning of display devices. Similarly, parameters like light levels, black bar widths, color bar types, telecine patterns, photosensitive epilepsy (PSE) levels and patterns, field order, scan types (interlaced or progressive), etc. also need validation before distribution. This paper explores critical video properties and demonstrates how computer vision, image processing, and machine learning techniques can measure and validate these properties and detect defects. Experiments utilizing machine learning (ML) techniques to quantify the quality degradation, due to lossy compression of video data, will also be discussed. A discussion of challenges and future directions to enhance the accuracy of measurement and detection is also included here.

Shekhar Madnani, Raman Kumar Gupta, Siddharth Gupta, Saurabh Jain | Interra Systems, Inc. | Cupertino, Calif., United States