5 papers
Multi-task Just Recognizable Difference for Video Coding for Machines: Database, Model, and Coding Application
Junqi Liu, Yun Zhang, Xiaoxia Huang +2
Just Recognizable Difference (JRD) boosts coding efficiency for machine vision through visibility threshold modeling, but is currently limited to a single-task scenario. To address…
Image Quality Assessment for Machines: Paradigm, Large-scale Database, and Models
Xiaoqi Wang, Yun Zhang, Weisi Lin
Machine vision systems (MVS) are intrinsically vulnerable to performance degradation under adverse visual conditions. To address this, we propose a machine-centric image quality as…
Deep Learning based Joint Geometry and Attribute Up-sampling for Large-Scale Colored Point Clouds
Yun Zhang, Feifan Chen, Na Li +4
Colored point cloud, which includes geometry and attribute components, is a mainstream representation enabling realistic and immersive 3D applications. To generate large-scale and…
No-Reference Image Quality Assessment with Global-Local Progressive Integration and Semantic-Aligned Quality Transfer
Xiaoqi Wang, Yun Zhang
Accurate measurement of image quality without reference signals remains a fundamental challenge in low-level visual perception applications. In this paper, we propose a global-loca…
DT-JRD: Deep Transformer based Just Recognizable Difference Prediction Model for Video Coding for Machines
Junqi Liu, Yun Zhang, Xiaoqi Wang +2
Just Recognizable Difference (JRD) represents the minimum visual difference that is detectable by machine vision, which can be exploited to promote machine vision oriented visual s…