Constant Subjective Quality Database: The Research and Device of Generating Video Sequences of Constant Quality
Dr Anastasia Mozhaeva, Eastern Institute of Technology
Dr Igor Vlasuyk, Moscow Technical University of Communications and Informatics
Dr Lee Streeter, The University Waikato
Associate Professor Michael Cree, The University Waikato
Aleksei Potashnikov, Moscow Technical University of Communications and Informatics
Primary contact: Anastasia Mozhaeva amozhaeva@eit.ac.nz
Abstract:
Video databases with the collection of subjective assessments form an essential basis for learning algorithms, testing, and creating an objective video quality assessment. In this work, a novel device to measure coded video quality was proposed using the finding of an acceptable minimum perception threshold, which allows one to generate video sequences of constant quality. Also, this work describes the method, which generates video sequences of continuous quality and a specialized installation that allows you to dynamically change the visual quality of typical media content during the examination process. This approach allows obtaining well-labelled data in a larger volume than the currently used binary estimates. The novel databases with this approach will be used to create effective adaptive codecs built according to the hybrid model and new ones based on machine learning. Here, we contribute to advancing the issue of streaming quality by creating a large-scale dataset with video compression and transmission artefacts. Our final dataset consists of 4.1 million video quality perceptual thresholds by users.
Materials:
Mozhaeva A, Potashnikov A, Vlasuyk I and Streeter L (2021). Constant Subjective Quality Database: The Research and Device of Generating Video Sequences of Constant Quality. International Conference on Engineering Management of Communication and Technology (EMCTECH), Vienna, Austria, 2021, pp. 1-5, doi: 10.1109/EMCTECH53459.2021.9618977.
Mozhaeva A, Mazin V, Cree MJ and Streeter L (2023). NRspttemVQA: Real-Time Video Quality Assessment Based on the User’s Visual Perception, 38th International Conference on Image and Vision Computing New Zealand (IVCNZ), Palmerston North, New Zealand, pp. 1-7. doi: 10.1109/IVCNZ61134.2023.10343863.
Structural diagram of the installation for research:
Code and data [Github]
Dr Anastasia Mozhaeva, Eastern Institute of Technology
Dr Igor Vlasuyk, Moscow Technical University of Communications and Informatics
Dr Lee Streeter, The University Waikato
Associate Professor Michael Cree, The University Waikato
Aleksei Potashnikov, Moscow Technical University of Communications and Informatics
Primary contact: Anastasia Mozhaeva amozhaeva@eit.ac.nz
Abstract:
Video databases with the collection of subjective assessments form an essential basis for learning algorithms, testing, and creating an objective video quality assessment. In this work, a novel device to measure coded video quality was proposed using the finding of an acceptable minimum perception threshold, which allows one to generate video sequences of constant quality. Also, this work describes the method, which generates video sequences of continuous quality and a specialized installation that allows you to dynamically change the visual quality of typical media content during the examination process. This approach allows obtaining well-labelled data in a larger volume than the currently used binary estimates. The novel databases with this approach will be used to create effective adaptive codecs built according to the hybrid model and new ones based on machine learning. Here, we contribute to advancing the issue of streaming quality by creating a large-scale dataset with video compression and transmission artefacts. Our final dataset consists of 4.1 million video quality perceptual thresholds by users.
Mozhaeva A, Potashnikov A, Vlasuyk I and Streeter L (2021). Constant Subjective Quality Database: The Research and Device of Generating Video Sequences of Constant Quality. International Conference on Engineering Management of Communication and Technology (EMCTECH), Vienna, Austria, 2021, pp. 1-5, doi: 10.1109/EMCTECH53459.2021.9618977.
Mozhaeva A, Mazin V, Cree MJ and Streeter L (2023). NRspttemVQA: Real-Time Video Quality Assessment Based on the User’s Visual Perception, 38th International Conference on Image and Vision Computing New Zealand (IVCNZ), Palmerston North, New Zealand, pp. 1-7. doi: 10.1109/IVCNZ61134.2023.10343863.
Structural diagram of the installation for research: