Cheng Chen

Title: **Cheng Chen: Innovator in Video Encoding Technologies**

Introduction

Cheng Chen, based in Milpitas, CA, has made significant contributions to the field of video encoding technologies. As an inventor with a keen focus on machine learning integration, he holds a noteworthy patent that enhances the efficiency of video encoding processes.

Latest Patents

Cheng Chen holds a patent titled "Rate control machine learning models with feedback control for video encoding." This patent encompasses methods, systems, and apparatus, including computer programs encoded on a computer storage medium, specifically designed for encoding video comprising a sequence of video frames. The innovative approach outlined in his patent involves obtaining a feature embedding for each video frame, processing it using a rate control machine learning model, selecting an optimal quantization parameter, and ensuring the video frame is efficiently encoded based on feedback control criteria.

Career Highlights

Cheng is currently employed at DeepMind Technologies Limited, a prominent company in the technology and artificial intelligence sector. His work focuses on developing advanced video coding solutions that leverage machine learning, showcasing his expertise and commitment to driving innovation in video processing technologies.

Collaborations

During his career, Cheng Chen has collaborated with talented individuals in the industry, including Chenjie Gu and Hongzi Mao. These collaborations are instrumental in pushing the boundaries of current video encoding methods and developing cutting-edge technologies that benefit various applications in the digital media landscape.

Conclusion

Cheng Chen's innovative contributions to video encoding through his patented methods demonstrate the power of integrating machine learning into traditional encoding processes. With his work at DeepMind Technologies Limited and collaborations with industry peers, Cheng continues to play a pivotal role in advancing video technology, paving the way for more efficient and effective encoding solutions in the future.