Sunnyvale, CA, United States of America

Advait Mogre Madhav


Average Co-Inventor Count = 5.0

ph-index = 1

Forward Citations = 4(Granted Patents)


Company Filing History:


Years Active: 2017

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1 patent (USPTO):Explore Patents

Title: Advait Mogre Madhav: Innovator in Video Compression Technology

Introduction

Advait Mogre Madhav is a notable inventor based in Sunnyvale, CA (US). He has made significant contributions to the field of digital video processing, particularly in the area of noise detection prior to video compression. His innovative approach addresses a critical challenge in video technology, enhancing the quality of digital media.

Latest Patents

Advait holds a patent for a "Method and system for detection of inherent noise present within a video source prior to digital video compression." This patent outlines a method for extracting noise images by subtracting current images from their filtered versions. The process involves normalizing each pixel of the extracted noise image based on a principal edge image. The invention accumulates analog noise pixels to generate an intermediate noise confidence value, which is crucial for detecting analog noise. The method also includes the detection of high-frequency noise, enhancing the overall efficiency of video compression.

Career Highlights

Advait Mogre Madhav is currently employed at Interra Systems, Inc., where he continues to develop innovative solutions in video technology. His work has positioned him as a key player in the industry, contributing to advancements that improve video quality and compression techniques.

Collaborations

Advait collaborates with talented professionals such as Bhupender Kumar and Pervez Alam. Their combined expertise fosters a creative environment that drives innovation within their projects.

Conclusion

Advait Mogre Madhav's contributions to video compression technology exemplify the impact of innovative thinking in the digital age. His patent and ongoing work at Interra Systems, Inc. highlight his commitment to enhancing video quality through advanced noise detection methods.

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