Skip to Main content Skip to Navigation

Quality assessment of stereoscopic 3D content based on binocular perception

Yu Fan 1
Abstract : The great advance of stereoscopic/3D technologies leads to a remarkable growth of 3D content in various applications thanks to a realistic and immersive experience. However, these technologies also brought some technical challenges and issues, regarding quality assessment and compression due to the complex processes of the binocular vision. Aiming to evaluate and optimize the performance of 3D imaging systems with respect to their storage capacity and quality of experience (QoE), this thesis focuses on two main parts: 1- spatial visibility thresholds of the human visual system (HVS) and 2- stereoscopic image quality assessment (SIQA). It is well-known that the HVS cannot detect the changes in a compressed image if these changes are lower than the just noticeable different (JND) threshold. Therefore, an extensive study based on objective and subjective analysis has been conducted on existing 3D-JND models. In addition, a new 3D-JND model has been proposed based on psychophysical experiments aiming to measure the effect of binocular disparity and spatial masking on the visual thresholds. In the second part, we explored new approaches for SIQA from two different perspectives. First, we developed a reference-based model accounting for both monocular and cyclopean quality. Then, we proposed a new blind quality metric relying on local contrast statistics combination of the stereopair. Both models considered the binocular fusion and binocular rivalry behaviors of the HVS in order to accurately simulate the human judgment of 3D quality.
Complete list of metadatas

Cited literature [501 references]  Display  Hide  Download
Contributor : Abes Star :  Contact
Submitted on : Monday, May 25, 2020 - 3:17:08 PM
Last modification on : Tuesday, May 26, 2020 - 3:37:24 AM


Version validated by the jury (STAR)


  • HAL Id : tel-02618691, version 1



Yu Fan. Quality assessment of stereoscopic 3D content based on binocular perception. Bioinformatics [q-bio.QM]. Université de Poitiers; Norwegian University Science Technology, 2019. English. ⟨NNT : 2019POIT2266⟩. ⟨tel-02618691⟩



Record views


Files downloads