Company Filing History:
Years Active: 2025
Title: Innovations in Image Compression: The Contributions of Inventor Yang Li
Introduction
Yang Li, an inventive mind based in Chapel Hill, North Carolina, has made significant strides in the field of image compression. With a unique approach that leverages actively-learned context modeling, he has secured a patent that showcases his innovative techniques.
Latest Patents
Yang Li's patent, titled "Actively-learned context modeling for image compression," outlines methodologies and systems that enhance the efficiency of image compression. This invention involves selecting a subset of data from a training dataset corresponding to the image needing compression. A context model is constructed in the form of a decision tree, utilizing entropy values to assess the diversity of context within each leaf node. This data-driven approach allows for the creation of an updated context model, optimizing the image compression process significantly.
Career Highlights
Currently, Yang Li is employed at Adobe Inc., a company known for its innovative contributions to digital media and design software. His work focuses on enhancing image processing technologies, which are critical in various applications spanning from graphic design to online media. Yang's expertise and dedication have positioned him as a valuable asset in his field.
Collaborations
At Adobe, Yang collaborates with distinguished colleagues, including Gang Wu and Stefano Petrangeli. Their collective efforts contribute to advancing technologies that redefine how images are processed and compressed, reflecting a commitment to innovation within the company.
Conclusion
With his inventive approaches and collaborative spirit, Yang Li exemplifies the role of contemporary inventors in pushing the boundaries of technology. His patent on actively-learned context modeling not only illustrates his contributions but also encourages ongoing advancements in image compression techniques. As technology continues to evolve, Yang's work remains integral to the future of digital imaging.