Key-Point Sequence Lossless Compression for Intelligent Video Analysis

TL;DR

This paper introduces a lossless compression method for key-point sequences in videos, significantly reducing data size by removing redundancies, thereby improving efficiency in intelligent video analysis.

Contribution

It proposes a novel predict-and-encode strategy with adaptive mode selection for lossless compression of key-point sequences in videos.

Findings

Effective reduction in data size demonstrated on multiple key-point sequence types.

Outperforms existing compression methods in preserving information without loss.

Validated through extensive experiments on widely used video analysis datasets.

Abstract

Feature coding has been recently considered to facilitate intelligent video analysis for urban computing. Instead of raw videos, extracted features in the front-end are encoded and transmitted to the back-end for further processing. In this article, we present a lossless key-point sequence compression approach for efficient feature coding. The essence of this predict-and-encode strategy is to eliminate the spatial and temporal redundancies of key points in videos. Multiple prediction modes with an adaptive mode selection method are proposed to handle key-point sequences with various structures and motion. Experimental results validate the effectiveness of the proposed scheme on four types of widely used key-point sequences in video analysis.