UVG-VCM: Benchmarking Dataset for Machine-Oriented Visual Data Compression
UVG-VCM is a new video dataset specifically designed for machine-oriented codec
evaluation. The dataset comprises 20 uncompressed and annotated video sequences distributed under the
open CC-BY 4.0 license, most of which in 4K, 60 fps, 16 bits YUV444
format. The sequences represent a diverse collection of realistic machine vision use cases, such as
object detection, tracking, segmentation, human pose estimation, depth estimation, and license plate
recognition. This dataset is intended to foster reproducible benchmarking of future machine oriented
video codecs.
Please cite the following paper for any usage of the dataset:
T. Partanen, M. Anttila, R. Kortelahti, G. Gautier, A. Mercat, and J. Vanne,
“UVG-VCM: Benchmarking dataset for machine-oriented visual data compression,” Accepted to
Int. Conf. Qual. Multimedia Exper., Cardiff, United Kingdom, Jun.–Jul. 2026.
About the annotations
Each sequence contains a YUV video file and machine task annotations. Depth map annotations and instance
segmentation masks are provided as per-frame PNG images, while all other annotations are stored in JSON
format, including polygon representations of the instance segmentation masks. Visualization is provided
for one task for each sequence.
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