activity
20192022
most citedPerceptually Optimizing Deep Image Compression

8 citations · 19 across the 6 of their papers we have counts for

collaborators

8 papers

eess.IV20225 cited

Estimating the Resize Parameter in End-to-end Learned Image Compression

Li-Heng Chen, Christos G. Bampis, Zhi Li +2

We describe a search-free resizing framework that can further improve the rate-distortion tradeoff of recent learned image compression models. Our approach is simple: compose a pai…

eess.IV2021

Convolutional Block Design for Learned Fractional Downsampling

Li-Heng Chen, Christos G. Bampis, Zhi Li +2

The layers of convolutional neural networks (CNNs) can be used to alter the resolution of their inputs, but the scaling factors are limited to integer values. However, in many imag…

cs.CV20213 cited

Regression or Classification? New Methods to Evaluate No-Reference Picture and Video Quality Models

Zhengzhong Tu, Chia-Ju Chen, Li-Heng Chen +4

Video and image quality assessment has long been projected as a regression problem, which requires predicting a continuous quality score given an input stimulus. However, recent ef…

eess.IV2020

Perceptual Video Quality Prediction Emphasizing Chroma Distortions

Li-Heng Chen, Christos G. Bampis, Zhi Li +2

Measuring the quality of digital videos viewed by human observers has become a common practice in numerous multimedia applications, such as adaptive video streaming, quality monito…

eess.IV20208 cited

Perceptually Optimizing Deep Image Compression

Li-Heng Chen, Christos G. Bampis, Zhi Li +2

Mean squared error (MSE) and norms have largely dominated the measurement of loss in neural networks due to their simplicity and analytical properties. However, when used…

cs.MM20203 cited

A Comparative Evaluation of Temporal Pooling Methods for Blind Video Quality Assessment

Zhengzhong Tu, Chia-Ju Chen, Li-Heng Chen +3

Many objective video quality assessment (VQA) algorithms include a key step of temporal pooling of frame-level quality scores. However, less attention has been paid to studying the…