arXiv:2404.09029v1 Announce Type: new
Abstract: Over the past two decades, the surge in video streaming applications has been fueled by the increasing accessibility of the internet and the growing demand for network video. As users with varying internet speeds and devices seek high-quality video, transcoding becomes essential for service providers. In this paper, we introduce a parametric rate-distortion (R-D) transcoding model. Our model excels at predicting transcoding distortion at various rates without the need for encoding the video. This model serves as a versatile tool that can be used to achieve visual quality improvement (in terms of PSNR) via trans-sizing. Moreover, we use our model to identify visually lossless and near-zero-slope bitrate ranges for an ingest video. Having this information allows us to adjust the transcoding target bitrate while introducing visually negligible quality degradations. By utilizing our model in this manner, quality improvements up to 2 dB and bitrate savings of up to 46% of the original target bitrate are possible. Experimental results demonstrate the efficacy of our model in video transcoding rate distortion prediction.
Parametric Rate-Distortion Transcoding Model for Video Streaming
In the realm of multimedia information systems, video streaming has become a prominent application due to the widespread accessibility of the internet and the growing demand for network video. As service providers strive to cater to users with varying internet speeds and devices, transcoding, which involves converting video formats, becomes crucial.
This paper introduces a parametric rate-distortion (R-D) transcoding model, which offers a novel approach to predicting transcoding distortion at different rates without the need for video encoding. This model serves as a versatile tool for achieving visual quality improvement through trans-sizing. By understanding the trade-off between rate and distortion, service providers can optimize the transcoding process and enhance video quality in terms of peak signal-to-noise ratio (PSNR).
Multi-Disciplinary Nature
This research presents a multi-disciplinary approach by bridging concepts from multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. The video transcoding model can be applied to various domains, such as virtual reality simulations, where high-quality video content is necessary for an immersive experience. By utilizing the parametric R-D transcoding model, developers can ensure that the video content meets the desired quality standards, enhancing the overall user experience.
Impact on Multimedia Information Systems
The parametric R-D transcoding model proposed in this paper contributes to the field of multimedia information systems by providing a method to optimize video quality without the need for encoding. This approach reduces computational complexity and time required for video transcoding, enabling service providers to deliver high-quality video streaming efficiently. The model’s ability to identify visually lossless and near-zero-slope bitrate ranges for an ingest video allows for adjustments in transcoding targets, resulting in bitrate savings while maintaining visually negligible quality degradations. This optimization not only benefits service providers but also ensures that users receive high-quality video content tailored to their internet speeds and devices.
Experimental Results and Insights
The experimental results presented in this research affirm the effectiveness of the parametric R-D transcoding model. Quality improvements of up to 2 dB and bitrate savings of up to 46% of the original target bitrate are achievable by using this model in the transcoding process. These findings highlight the potential impact of the model on the efficiency and quality of video streaming services. As video streaming continues to grow in popularity, optimizing transcoding techniques becomes increasingly important in meeting the expectations of users with diverse internet capabilities and consuming devices.
Future Directions
Building upon this research, future directions could involve exploring the application of the parametric R-D transcoding model in emerging technologies such as virtual and augmented reality. As these technologies advance, the demand for high-quality video content will continue to rise. By integrating the model into the transcoding pipeline of virtual and augmented reality systems, developers can ensure that the immersive experience is augmented by visually compelling video content. Additionally, further research could focus on refining the model to account for specific characteristics and requirements of different video streaming applications, thus enabling even more accurate rate-distortion prediction.