This paper investigates the efficacy of jointly optimizing content-specific
post-processing filters to adapt a human oriented video/image codec into a
codec suitable for machine vision tasks. By observing that artifacts produced
by video/image codecs are content-dependent, we propose a novel training
strategy based on competitive learning principles. This strategy assigns
training samples to filters dynamically, in a fuzzy manner, which further
optimizes the winning filter on the given sample. Inspired by simulated
annealing optimization techniques, we employ a softmax function with a
temperature variable as the weight allocation function to mitigate the effects
of random initialization. Our evaluation, conducted on a system utilizing
multiple post-processing filters within a Versatile Video Coding (VVC) codec
framework, demonstrates the superiority of content-specific filters trained
with our proposed strategies, specifically, when images are processed in
blocks. Using VVC reference software VTM 12.0 as the anchor, experiments on the
OpenImages dataset show an improvement in the BD-rate reduction from -41.3% and
-44.6% to -42.3% and -44.7% for object detection and instance segmentation
tasks, respectively, compared to independently trained filters. The statistics
of the filter usage align with our hypothesis and underscore the importance of
jointly optimizing filters for both content and reconstruction quality. Our
findings pave the way for further improving the performance of video/image
codecs.

通过优化内容特定的后处理滤波器，将面向人类的视频 / 图像编解码器调整为适用于机器视觉任务的编解码器，并且通过竞争学习原理，提出了一种基于模拟退火优化技术的训练策略，该策略以模糊方式动态地分配训练样本给滤波器，使用温度变量的 softmax 函数作为权重分配函数。评估实验结果表明，通过我们提出的策略训练的内容特定滤波器，在块状图像处理时相较于独立训练的滤波器，在物体检测和实例分割任务中，相比之下，较独立训练的滤波器能够提高 BD-rate 约为 - 42.3% 和 - 44.7%，并且滤波器使用的统计数据支持我们的假设，强调了同时优化内容和重构质量的滤波器的重要性，这些发现为进一步提高视频 / 图像编解码器的性能铺平了道路。

用于机器视频编码的竞争学习实现内容特定滤波器

Competitive Learning for Achieving Content-specific Filters in Video  Coding for Machines

Point cloud compression has garnered significant interest in computer vision.
However, existing algorithms primarily cater to human vision, while most point
cloud data is utilized for machine vision tasks. To address this, we propose a
point cloud compression framework that simultaneously handles both human and
machine vision tasks. Our framework learns a scalable bit-stream, using only
subsets for different machine vision tasks to save bit-rate, while employing
the entire bit-stream for human vision tasks. Building on mainstream
octree-based frameworks like VoxelContext-Net, OctAttention, and G-PCC, we
introduce a new octree depth-level predictor. This predictor adaptively
determines the optimal depth level for each octree constructed from a point
cloud, controlling the bit-rate for machine vision tasks. For simpler tasks
(\textit{e.g.}, classification) or objects/scenarios, we use fewer depth levels
with fewer bits, saving bit-rate. Conversely, for more complex tasks
(\textit{e.g}., segmentation) or objects/scenarios, we use deeper depth levels
with more bits to enhance performance. Experimental results on various datasets
(\textit{e.g}., ModelNet10, ModelNet40, ShapeNet, ScanNet, and KITTI) show that
our point cloud compression approach improves performance for machine vision
tasks without compromising human vision quality.

提出了一种点云压缩框架，可以同时处理人类视觉和机器视觉任务，并通过使用自适应的八叉树深度级别预测器来控制位速，以提高机器视觉任务的性能，而不影响人类视觉质量。