Inlämning av Examensarbete / Submission of Thesis

Amitesh Kumar Singam and Venkat Raj Reddy Pashike , pp. 66. ING/School of Engineering, 2012.

The work

Författare / Author: Amitesh Kumar Singam and Venkat Raj Reddy Pashike
Titel / Title: Low Complex Blind Video Quality Predictor based on Support Vector Machines
Abstrakt Abstract:

Objective Video Quality Assessment plays a vital role in visual processing systems and especially in the mobile communication field, some of the video applications boosted the interest of robust methods for quality assessment. Out of all existing methods for Video Quality Analysis, No-Reference (NR) Video Quality Assessment is the one which is most needed in situations where the handiness of reference video is not available. Our challenge lies in formulating and melding effective features into one model based on human visualizing characteristics. Our research work explores the tradeoffs between quality prediction and complexity of a system. Therefore, we implemented support vector regression algorithm as NR-based Video Quality Metric(VQM) for quality estimation with simplified input features. The features are obtained from extraction of H.264 bitstream data at the decoder side of the network. Our metric predicted with Pearson correlation coefficient of 0.99 for SSIM, 0.98 for PEVQ, 0.96 for subjective score and 0.94 for PSNR metric. Therefore in terms of prediction accuracy, the proposed model has good correlation with all deployed metrics and the obtained results demonstrates the robustness of our approach. In our research work, the proposed metric has a good correlation with subjective scores which concludes that proposed metric can be employed for real time use, since subjective scores are considered as true or standard values of video quality.

Ämnesord / Subject: Telekommunikation - Telecommunications
Signalbehandling - Signal Processing
Datavetenskap - Computer Science\Artificial Intelligence
Nyckelord / Keywords: VQM, VQE, SVM, PCA, PSNR, SSIM, MOS and PEVQ.

Publication info

Dokument id / Document id: houn-8vnvkq
Program:/ Programme Magisterprogram i Elektroteknik / Master of Science in Electrical Engineering
Registreringsdatum / Date of registration: 06/28/2012
Uppsatstyp / Type of thesis: Masterarbete/Master's Thesis (120 credits)

Context

Handledare / Supervisor: Muhammad Shahid
muhammad.shahid@bth.se
Examinator / Examiner: Benny Lövström
Organisation / Organisation: Blekinge Institute of Technology
Institution / School: ING/School of Engineering

+46 455 38 50 00

Files & Access

Bifogad uppsats fil(er) / Files attached: bth2012kumar.pdf (955 kB, öppnas i nytt fönster)