BVI-DVC: A Training Database for Deep Video Compression

Di Ma, Fan Zhang and David Bull

ABSTRACT

Deep learning methods are increasingly being applied in the optimisation of video compression algorithms and can achieve significantly enhanced coding gains, compared to conventional approaches. Such approaches often employ Convolutional Neural Networks (CNNs) which are trained on databases with relatively limited content coverage. In this paper, a new extensive and representative video database, BVI-DVC, is presented for training CNN-based coding tools. BVI-DVC contains 800 sequences at various spatial resolutions from 270p to 2160p and has been evaluated on ten existing network architectures for four different coding tools. Experimental results show that the database produces significant improvements in terms of coding gains over three existing (commonly used) image/video training databases, for all tested CNN architectures under the same training and evaluation configurations.

SOURCE EXAMPLES

 

[DOWNLOAD] all videos from University of Bristol Research Data Storage Facility.

If this content has been mentioned in a research publication, please give credit to the University of Bristol, by referencing:

[1] Di Ma, Fan Zhang and David Bull, “BVI-DVC: A Training Database for Deep Video Compression“, arXiv:2003.13552, 2020.

[2] Di Ma, Fan Zhang and David Bull,”BVI-DVC“, 2020.

 

 

Computational cameras

A novel close-to-sensor computational camera has been designed and developed at the ViLab. ROIs can be captured and processed at 1000fps; the concurrent processing enables low latency sensor control and flexible image processing. With 9DoF motion sensing, the low, size, weight and power form-factor makes it ideally suited for robotics and UAV applications. The modular design allows multiple configurations and output options, easing development of embedded applications. General purpose output can directly interface with external devices such as servos and motors while ethernet offers a conventional image output capability. A binocular system can be configured with self-driven pan/tilt positioning, as an autonomous verging system or as a standard stereo pair. More information can be found here xcamflyer.

Monitoring Vehicle Occupants

Visual Monitoring of Driver and Passenger Control Panel Interactions

Researchers

Toby Perrett and Prof. Majid Mirmehdi

Overview

Advances in vehicular technology have resulted in more controls being incorporated in cabin designs. We present a system to determine which vehicle occupant is interacting with a control on the centre console when it is activated, enabling the full use of dual-view touchscreens and the removal of duplicate controls. The proposed method relies on a background subtraction algorithm incorporating information from a superpixel segmentation stage. A manifold generated via the diffusion maps process handles the large variation in hand shapes, along with determining which part of the hand interacts with controls for a given gesture. We demonstrate superior results compared to other approaches on a challenging dataset.

Examples

Some example interactions with the dashboard of a Range Rover using near infra-red illumination and a near infra-red pass filter:

Some sample paths through a 3D manifold. The top row of images correspond to a clockwise dial turn. The middle row corresponds to a button press with the index finger, and the bottom shows how finer details such as a thumb extending can be determined:

manifoldpaths3 

References

This work has been accepted for publication in IEEE Transactions on Intelligent Transportation Systems. It is open access and can be downloaded from here:

http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7486959

Remote Pulmonary Function Testing using a Depth Sensor

We propose a remote non-invasive approach to Pulmonary Function Testing using a time-of-flight depth sensor (Microsoft Kinect V2), and correlate our results to clinical standard spirometry results. Given point clouds, we approximate 3D models of the subject’s chest, estimate the volume throughout a sequence and construct volume-time and flow-time curves for two prevalent spirometry tests: Forced Vital Capacity and Slow Vital Capacity. From these, we compute clinical measures, such as FVC, FEV1, VC and IC. We correlate automatically extracted measures with clinical spirometry tests on 40 patients in an outpatient hospital setting. These demonstrate high within-test correlations.

 

V. Soleimani, M. Mirmehdi, D. Dame, S. Hannuna, M. Camplani, J. Viner and J. Dodd “Remote pulmonary function testing using a depth sensor,” Biomedical Circuits and Systems Conference (BioCAS), 2015 IEEE, Atlanta, GA, 2015, pp. 1-4.
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7348445&isnumber=7348273