Hojatollah Yeganeh

This inventor holds 7 USPTO granted patents and 1 published patent application and 2 EPO patents. Top assignees: Imax Corporation, Ssimwave Inc.. Active years: 2022-2026.

IDiyas Innovation Intelligence. (2026). Inventor Profile: Hojatollah Yeganeh. Retrieved from https://idiyas.com/inventor/hojatollah-yeganeh

Data source: USPTO (United States Patent and Trademark Office) public patent records. Weekly synchronization. How IDiyas builds this profile

USPTO Granted Patents = 7 (Patent Applications = 1)

% Patents Active = 85.7

EPO Patents = 2 (Patent Applications = 7)

Average Co-Inventor Count = 5.7

ph-index = 2

Forward Citations = 6(Granted Patents)

Company Filing History:

Years Active: 2022-2026

Loading Chart...

7 patents (USPTO):Sort by Forward CitationsExplore Patents

1. 12701238 - Video asset quality assessment and encoding optimization to achieve target quality requirement (2026-08-04),

2. 12463757 - Automatic visual media transmission error assessment (2025-11-04),

3. 12333741 - No-reference visual media assessment combining deep neural networks and models of human visual system and video content/distortion analysis (2025-06-17),

4. 12262097 - Unified end-to-end quality and latency measurement, optimization and management in multimedia communications (2025-03-25),

5. 11638051 - Real-time latency measurement of video streams (2023-04-25),

6. 11477351 - Image and video banding assessment (2022-10-18),

7. 11363345 - Unified end-to-end quality and latency measurement, optimization and management in multimedia communications (2022-06-14),

Title: Hojatollah Yeganeh: Innovator in Visual Media Transmission

Introduction

Hojatollah Yeganeh is a prominent inventor based in Waterloo, Canada. He has made significant contributions to the field of visual media transmission, holding a total of 7 patents. His work focuses on assessing transmission errors in visual media, utilizing advanced techniques in deep neural networks.

Latest Patents

Yeganeh's latest patents include innovative methods for automatic visual media transmission error assessment. This system assesses transmission errors by analyzing visual media inputs through content analysis, codec analysis, distortion analysis, and human visual system modeling. The visual media input is divided into partitions, which are processed by deep neural networks (DNNs). The outputs from these partitions are combined with domain knowledge to produce a comprehensive assessment of transmission errors.

Another notable patent involves no-reference visual media assessment, which provides a quality assessment of test visual media inputs. This method decomposes the test video into multiple-channel representations and utilizes domain knowledge obtained from various analyses. The DNN outputs are then combined to generate an overall quality score for the visual media input.

Career Highlights

Throughout his career, Yeganeh has worked with notable companies such as Ssimwave Inc. and Imax Corporation. His expertise in visual media technology has positioned him as a key player in the industry, contributing to advancements in quality assessment and error detection.

Collaborations

Yeganeh has collaborated with esteemed colleagues, including Zhou Wang and Kai Zeng. Their joint efforts have furthered research and development in the field of visual media transmission.

Conclusion

Hojatollah Yeganeh's innovative work in visual media transmission error assessment showcases his dedication to advancing technology in this field. His contributions through patents and collaborations highlight his impact on the industry.

Profile summary based on public USPTO records.