With several advantages over conventional RGB cameras, event cameras have provided new opportunities for tackling visual tasks under challenging scenarios with fast motion, high dynamic range, and/or power constraint. Yet unlike image/video compression, the performance of event compression algorithm is far from satisfying and practical. The main challenge for compressing events is the unique event data form, i.e., a stream of asynchronously fired event tuples each encoding the 2D spatial location, timestamp, and polarity (denoting an increase or decrease in brightness). Since events only encode temporal variations, they lack spatial structure which is crucial for compression. To address this problem, we propose a novel event compression algorithm based on a quad tree (QT) segmentation map derived from the adjacent intensity images. The QT informs 2D spatial priority within the 3D space-time volume. In the event encoding step, events are first aggregated over time to form polarity-based event histograms. The histograms are then variably sampled via Poisson Disk Sampling prioritized by the QT based segmentation map. Next, differential encoding and run length encoding are employed for encoding the spatial and polarity information of the sampled events, respectively, followed by Huffman encoding to produce the final encoded events. Our Poisson Disk Sampling based Lossy Event Compression (PDS-LEC) algorithm performs rate-distortion based optimal allocation. On average, our algorithm achieves greater than 6x compression compared to the state of the art.
翻译:与常规 RGB 相机相比, 事件相机比常规的 RGB 相机具有一些优势, 为在具有挑战性的情景下处理视觉任务提供了新的机会。 但是, 与图像/ 视频压缩不同, 事件压缩算法的性能远非满足和实用。 压缩事件的主要挑战在于独特的事件数据形式, 即: 一个不同步的发射事件图例流, 每将 2D 的空间位置、 时间戳和极度编码( 显示亮度的增减 ) 。 由于事件只是将时间变化编码化, 它们缺乏对压缩至关重要的空间结构。 为了解决这个问题, 我们提议了一个基于 QT 的四边树( QT) 分割图的新型事件压缩算法。 压缩事件的主要挑战在于: QT 在 3D 空间时段内, 星际点显示 2D 空间优先度 。 在时间序列中, 事件首先汇总成以极地基事件为基的直线图。 然后通过 Poisson Disk Sampl 优先采集的空间结构结构结构结构结构结构。, 以我们以更高级的平局 的平局 递校正压为基 的校正序为基 。