6 papers
Emerging Standards for Machine-to-Machine Video Coding
Md Eimran Hossain Eimon, Velibor Adzic, Hari Kalva +1
Machines are increasingly becoming the primary consumers of visual data, yet most deployments of machine-to-machine systems still rely on remote inference where pixel-based video i…
Feature Coding for Scalable Machine Vision
Md Eimran Hossain Eimon, Juan Merlos, Ashan Perera +3
Deep neural networks (DNNs) drive modern machine vision but are challenging to deploy on edge devices due to high compute demands. Traditional approaches-running the full model on-…
ROI-Packing: Efficient Region-Based Compression for Machine Vision
Md Eimran Hossain Eimon, Alena Krause, Ashan Perera +4
This paper introduces ROI-Packing, an efficient image compression method tailored specifically for machine vision. By prioritizing regions of interest (ROI) critical to end-task ac…
Efficient Feature Compression for Machines with Global Statistics Preservation
Md Eimran Hossain Eimon, Hyomin Choi, Fabien Racapé +4
The split-inference paradigm divides an artificial intelligence (AI) model into two parts. This necessitates the transfer of intermediate feature data between the two halves. Here,…
Enabling Next-Generation Consumer Experience with Feature Coding for Machines
Md Eimran Hossain Eimon, Juan Merlos, Ashan Perera +3
As consumer devices become increasingly intelligent and interconnected, efficient data transfer solutions for machine tasks have become essential. This paper presents an overview o…
New VVC profiles targeting Feature Coding for Machines
Md Eimran Hossain Eimon, Ashan Perera, Juan Merlos +2
Modern video codecs have been extensively optimized to preserve perceptual quality, leveraging models of the human visual system. However, in split inference systems-where intermed…