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One of the main targets in video surveillance systems is to detect and possibly identify objects within monitoring range. This entails analyzing the video stream, by applying object detection techniques on one or more frames. Regardless of the output, the stream is usually archived for future use. Real-time requirements, network bandwidth, and storage constraints play a significant role to total performance. As video resolution increases, so does the video stream size. To harness such an increase, newer video compression standards offer sophisticated coding tools that aim at reducing video size, with minimal quality loss. However, as the achievable compression ratio increases, so does the computational complexity. In this paper, we propose a methodology to reduce both bitrate and processing time of video surveillance streams whereby object detection is performed. The method takes advantage of tile partitioning, with the aim of (i) reducing the scope and the invocation frequency of the object detection module, (ii) encoding different blocks of a frame at different quality levels, depending on whether objects exist or not, and (iii) encoding and transmitting only tiles containing objects. Experimental results using the UA-DETRAC dataset and the VVenC encoder demonstrate that exploiting tile partitioning in the manner proposed in the paper results in reducing bitrate and processing time at the expense of only tiny losses in accuracy. Full article

The newest video compression standard, Versatile Video Coding (VVC), was finalized in July 2020 by the Joint Video Experts Team (JVET). Its main goal is to reduce the bitrate by 50% over its predecessor video coding standard, the High Efficiency Video Coding (HEVC). Due to the new advanced tools and features included in VVC, it actually provides high coding performances—for instance, the Quad Tree with nested Multi-Type Tree (QTMTT) involved in the partitioning block. Furthermore, VVC introduces various techniques that allow for superior performance compared to HEVC, but with an increase in the computational complexity. To tackle this complexity, a fast Coding Unit partition algorithm based on machine learning for the intra configuration in VVC is proposed in this work. The proposed algorithm is formed by five binary Light Gradient Boosting Machine (LightGBM) classifiers, which can directly predict the most probable split mode for each coding unit without passing through the exhaustive process known as Rate Distortion Optimization (RDO). These LightGBM classifiers were offline trained on a large dataset; then, they were embedded on the optimized implementation of VVC known as VVenC. The results of our experiment show that our proposed approach has good trade-offs in terms of time-saving and coding efficiency. Depending on the preset chosen, our approach achieves an average time savings of 30.21% to 82.46% compared to the VVenC encoder anchor, and a Bjøntegaard Delta Bitrate (BDBR) increase of 0.67% to 3.01%, respectively. Full article