QA-Filter: A QP-Adaptive Convolutional Neural Network Filter for Video Coding

Learning to Restore Compressed Point Cloud Attribute: A Fully Data-Driven Approach and a Rules-Unrolling-Based Optimization

The emergence of holographic media drives the standardization of Geometry-based Point Cloud Compression (G-PCC) to sustain networked service provisioning. However, G-PCC inevitably introduces visually annoying artifacts, degrading the quality of experience (QoE). This work focuses on restoring G-PCC compressed point cloud attributes, e.g., RGB colors, to which fully data-driven and rules-unrolling-based post-processing filters are studied. At first, as compressed attributes exhibit nested blockiness, we develop a learning-based sample adaptive offset (NeuralSAO), which leverages a neural model using multiscale feature aggregation and embedding to characterize local correlations for quantization error compensation. Later, given statistically Gaussian distributed quantization noise, we suggest the utilization of a bilateral filter with Gaussian kernels to weigh neighbors by jointly considering their geometric and photometric contributions for restoration. Since local signals often present varying distributions, we propose estimating the smoothing parameters of the bilateral filter using an ultra-lightweight neural model. Such a bilateral filter with learnable parameters is called NeuralBF. The proposed NeuralSAO demonstrates the state-of-art restoration quality improvement, e.g., 20% BD-BR (Bjøntegaard delta rate) reduction over G-PCC on solid points clouds. However, NeuralSAO is computationally intensive and may suffer from poor generalization. On the other hand, although NeuralBF only achieves half of the gains of NeuralSAO, it is lightweight and exhibits impressive generalization across various samples. This comparative study between the data-driven large-scale NeuralSAO and the rules-unrolling-based small-scale NeuralBF helps to understand the capacity (i.e., performance, complexity, generalization) of underlying filters in terms of the quality restoration for compressed point cloud attribute.

Adaptive QP algorithm for depth range prediction and encoding output in virtual reality video encoding process

In order to reduce the encoding complexity and stream size, improve the encoding performance and further improve the compression performance, the depth prediction partition encoding is studied in this paper. In terms of pattern selection strategy, optimization analysis is carried out based on fast strategic decision-making methods to ensure the comprehensiveness of data processing. In the design of adaptive strategies, different adaptive quantization parameter adjustment strategies are adopted for the equatorial and polar regions by considering the different levels of user attention in 360 degree virtual reality videos. The purpose is to achieve the optimal balance between distortion and stream size, thereby managing the output stream size while maintaining video quality. The results showed that this strategy achieved a maximum reduction of 2.92% in bit rate and an average reduction of 1.76%. The average coding time could be saved by 39.28%, and the average reconstruction quality was 0.043, with almost no quality loss detected by the audience. At the same time, the model demonstrated excellent performance in sequences of 4K, 6K, and 8K. The proposed deep partitioning adaptive strategy has significant improvements in video encoding quality and efficiency, which can improve encoding efficiency while ensuring video quality.

RetinaMOT: rethinking anchor-free YOLOv5 for online multiple object tracking

In recent years, YOLOv5 networks have become a research focus in many fields because they are capable of outperforming state-of-the-art (SOTA) approaches in different computer vision tasks. Nevertheless, there is still room for improvement in YOLOv5 in terms of target tracking. We modified YOLOv5 according to the anchor-free paradigm to be on par with other state-of-the-art tracking paradigms and modified the network backbone to design an efficient module, thus proposing the RetinaYOLO detector, which, after combining state-of-the-art tracking algorithms, achieves state-of-the-art performance: we call it RetinaMOT. To the best of our knowledge, RetinaMOT is the first such approach. The anchor-free paradigm SOTA method for the YOLOv5 architecture and RetinaYOLO outperforms all lightweight YOLO architecture methods on the MS COCO dataset. In this paper, we show the details of the RetinaYOLO backbone, embedding Kalman filtering and the Hungarian algorithm into the network, with one framework used to accomplish two tasks. Our RetinaMOT shows that MOTA metrics reach 74.8, 74.1, and 66.8 on MOT Challenge MOT16, 17, and 20 test datasets, and our method is at the top of the list when compared with state-of-the-art methods.