64 citations · 146 across the 12 of their papers we have counts for
12 papers
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…
Banding vs. Quality: Perceptual Impact and Objective Assessment
Lukáš Krasula, Zhi Li, Christos G. Bampis +3
Staircase-like contours introduced to a video by quantization in flat areas, commonly known as banding, have been a long-standing problem in both video processing and quality asses…
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…
Enhancing VMAF through New Feature Integration and Model Combination
Fan Zhang, Angeliki Katsenou, Christos Bampis +3
VMAF is a machine learning based video quality assessment method, originally designed for streaming applications, which combines multiple quality metrics and video features through…
Strategy for Boosting Pair Comparison and Improving Quality Assessment Accuracy
Suiyi Ling, Jing Li, Anne Flore Perrin +3
The development of rigorous quality assessment model relies on the collection of reliable subjective data, where the perceived quality of visual multimedia is rated by the human ob…
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…
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…
GPM: A Generic Probabilistic Model to Recover Annotator's Behavior and Ground Truth Labeling
Jing Li, Suiyi Ling, Junle Wang +2
In the big data era, data labeling can be obtained through crowdsourcing. Nevertheless, the obtained labels are generally noisy, unreliable or even adversarial. In this paper, we p…
ProxIQA: A Proxy Approach to Perceptual Optimization of Learned Image Compression
Li-Heng Chen, Christos G. Bampis, Zhi Li +2
The use of norms has largely dominated the measurement of loss in neural networks due to their simplicity and analytical properties. However, when used to assess…
Towards Perceptually Optimized End-to-end Adaptive Video Streaming
Christos G. Bampis, Zhi Li, Ioannis Katsavounidis +3
Measuring Quality of Experience (QoE) and integrating these measurements into video streaming algorithms is a multi-faceted problem that fundamentally requires the design of compre…
SpatioTemporal Feature Integration and Model Fusion for Full Reference Video Quality Assessment
Christos G. Bampis, Zhi Li, Alan C. Bovik
Perceptual video quality assessment models are either frame-based or video-based, i.e., they apply spatiotemporal filtering or motion estimation to capture temporal video distortio…
Recover Subjective Quality Scores from Noisy Measurements
Zhi Li, Christos G. Bampis
Simple quality metrics such as PSNR are known to not correlate well with subjective quality when tested across a wide spectrum of video content or quality regime. Recently, efforts…