5 papers
ResPCC: A Loss-Resilient Neural Point Cloud Codec over Lossy Networks
Xueqin Niu, Mufan Liu, Yifan Wang +3
Point cloud compression (PCC) is critical for efficient storage and transmission of 3D data. While recent learning-based PCC methods achieve good rate-distortion (R-D) performance,…
Progressively Deformable 2D Gaussian Splatting for Video Representation at Arbitrary Resolutions
Mufan Liu, Qi Yang, Miaoran Zhao +4
Implicit neural representations (INRs) enable fast video compression and effective video processing, but a single model rarely offers scalable decoding across rates and resolutions…
Video Streaming with Kairos: An MPC-Based ABR with Streaming-Aware Throughput Prediction
Ziyu Zhong, Mufan Liu, Le Yang +3
In this paper, we present Kairos, a model predictive control (MPC)-based adaptive bitrate (ABR) scheme that integrates streaming-aware throughput predictions to enhance video strea…
Once-Training-All-Fine: No-Reference Point Cloud Quality Assessment via Domain-relevance Degradation Description
Yipeng Liu, Qi Yang, Yujie Zhang +4
The visual quality of point clouds plays a crucial role in the development and broadcasting of immersive media. Therefore, investigating point cloud quality assessment (PCQA) is in…
From Images to Point Clouds: An Efficient Solution for Cross-media Blind Quality Assessment without Annotated Training
Yipeng Liu, Qi Yang, Yujie Zhang +3
We present a novel quality assessment method which can predict the perceptual quality of point clouds from new scenes without available annotations by leveraging the rich prior kno…