Chien-Min Huang

Title: Innovations of Chien-Min Huang

Introduction

Chien-Min Huang is a notable inventor based in Logan, Utah. He has made significant contributions to the field of image compression technology. With a total of three patents to his name, Huang's work has advanced the efficiency and effectiveness of video communication systems.

Latest Patents

One of Huang's latest patents is titled "Bit allocation for sequence image compression." This invention relates to a method and system for allocating bits for representing blocks transmitted in an image compression system. In a preferred embodiment, a video telephone communications system includes a video transmitting/receiving system with local and remote processing systems separated by a communications link. The local processing system determines indices to be sent to a remote processing system to represent the image. The optimal bit allocation minimizes the relationship DR=D+.lambda.R, where D is the total distortion and R is the desired total number of bits for the frame.

Another significant patent is "Hierarchical adaptive multistage vector quantization." This invention describes an N-stage vector quantizer that compares increasingly smaller portions of a vector to a threshold until the quantization error is less than the threshold. The process ensures a constant bit rate and involves multiple stages of vector quantization to achieve optimal results.

Career Highlights

Chien-Min Huang is affiliated with Utah State University, where he continues to contribute to research and innovation in his field. His work has been instrumental in developing advanced technologies that enhance video communication.

Collaborations

Huang has collaborated with notable colleagues, including Paul D. Israelsen and Richard W. Harris. Their combined expertise has furthered the impact of their research and innovations.

Conclusion

Chien-Min Huang's contributions to image compression technology demonstrate his commitment to advancing communication systems. His patents reflect a deep understanding of the complexities involved in video transmission and compression.