Tampere, Finland

Xingyang Ni


 

Average Co-Inventor Count = 3.5

ph-index = 2

Forward Citations = 15(Granted Patents)


Company Filing History:


Years Active: 2021-2023

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6 patents (USPTO):Explore Patents

Title: The Innovations of Xingyang Ni

Introduction

Xingyang Ni is a prominent inventor based in Tampere, Finland. He has made significant contributions to the field of video coding and decoding, holding a total of six patents. His work focuses on enhancing the efficiency and effectiveness of media content processing through advanced neural network techniques.

Latest Patents

Xingyang Ni's latest patents include an innovative apparatus, method, and computer program for video coding and decoding. This method involves obtaining a configuration of at least one neural network with multiple intra-prediction mode agnostic layers and specific layers corresponding to different intra-prediction modes. The process includes determining to encode blocks using intra prediction, grouping blocks with the same intra-prediction mode, and training the neural networks based on a training loss. Another notable patent is related to content-specific neural network distribution, where media content is streamed to user devices. This involves training a neural network on portions of media content and updating its weights to optimize performance.

Career Highlights

Xingyang Ni is currently employed at Nokia Technologies Oy, where he continues to push the boundaries of innovation in video technology. His work at Nokia has positioned him as a key player in the development of advanced media processing solutions.

Collaborations

Xingyang collaborates with talented coworkers such as Francesco Cricri and Emre Baris Aksu. Their combined expertise contributes to the success of their projects and the advancement of technology in their field.

Conclusion

Xingyang Ni's contributions to video coding and decoding through his innovative patents and work at Nokia Technologies Oy highlight his significant role in the tech industry. His advancements in neural network applications are paving the way for more efficient media processing solutions.

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