Jin Soo Park

Title: Innovations of Jin Soo Park in Video Compression Technology

Introduction

Jin Soo Park is an accomplished inventor based in Austin, TX (US). He has made significant contributions to the field of video compression technology, particularly through the application of machine learning systems. With a total of 2 patents, his work is paving the way for advancements in how video data is processed and transmitted.

Latest Patents

One of Jin Soo Park's latest patents focuses on a video compression technique that utilizes a machine learning system. This computer-implemented method involves partitioning a set of video frames into two subsets of different types of frames: a first type and a second type. The first type of frames is compressed to generate a first representation using a first stage encoder. This representation is then decoded to reconstruct the first type of frames with a first stage decoder. Meanwhile, the second type of frames is compressed to create a second representation that contains only soft edge information, achieved through a second stage encoder. A generative model corresponding to a second stage decoder is trained using the first representation and the reconstructed first type of frames, employing a discriminator within the machine learning system. After training, the generative model generates reconstructed first and second types of frames using the soft edge information.

Career Highlights

Jin Soo Park is affiliated with the University of Texas System, where he continues to innovate and contribute to research in video technology. His work is recognized for its potential to enhance video quality and efficiency, making it a valuable asset in various applications.

Collaborations

Jin Soo Park has collaborated with notable colleagues, including Alan C Bovik and Sungsoo Kim. Their combined expertise in the field of video processing and machine learning has led to groundbreaking advancements in technology.

Conclusion

Jin Soo Park's contributions to video compression technology exemplify the intersection of innovation and practical application. His work not only enhances video quality but also demonstrates the potential of machine learning in transforming how we handle video data.