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Assessment of perceived image quality for smartphones with embedded camera


Author: Zorea Pinchas
Degree:doctor of engineering
Speciality: 05.13.07 - Automations and control of technological processes (according to branches)
Year:2018
Scientific advisers: Tudor Brăgaru
doctor, associate professor (docent), Moldova State University
Florentin Paladi
doctor habilitat, professor, Moldova State University
Institution: Moldova State University

Status

The thesis was presented on the 14 February, 2018
Approved by NCAA on the 11 May, 2018

Abstract

Adobe PDF document – 1.68 Mb / in romanian
Adobe PDF document – 1.78 Mb / in english

Keywords

: human visual test (HVT), perceived image quality (PIQ), image quality attributes (IQAs), mean opinion score (MOS), objective/subjective image quality assessment, video quality experts group (VQEG), VQEG image quality evaluation tool (VIQET)

Summary

Thesis structure: The thesis contains Introduction, 3 chapters, general conclusions and recommendations, bibliography of 101 titles. The main text amounts to 107 pages, includes 66 figures, 30 tables, 37 formulas, and 5 appendixes. The obtained results of the thesis were published in 7 scientific papers.

Aim of research is to develop a new no or zero (NR) reference subjective quality assessment model and framework that would enable the smartphones industry prediction of the PIQ.

The objectives of thesis include the design of NR subjective and objective image quality metrics based on extensive visual tests experiments and evaluation by SW tool VIQET that measure the perceived image quality of smartphones users.

Scientific novelty and originalityof the obtained results is reflected in a new approach which predicts the PIQ by extraction of the classic, objective IQAs (brightness, contrast, color saturation and sharpness) to new subjective IQ criteria that evaluated by VIQET. This is a new assessment process based on a new diploid model, which aims to predict how the smartphones users perceived image quality.

Important scientific solved problem consists in elaborating a new diploid model for PIQ assessment that describes how to extract the standard IQAs which used in large screens TVs into new IQAs of SW tool VIQET. This can be used by smartphone producers and vendors in order to shorten „time to market”, or by any image quality experts, including in the academia in order to reduce time and cost of PIQ assessment process of smartphones with small HD displays in comparison to the process that based on many human physical tests.

Theoretical significance is supported by analyzing, specifying and defining the theoretical principles and new PIQ criteria which implemented in the SW tool VIQET in order to predict the expected MOS in HVTs. The new model determines the relationship between the standard IQAs values and the VIQET PIQ criteria.

Applicative value of the work is determined by the developed model, which has huge potential for smartphone industry and users in reducing significantly the time and cost of PIQ assessment.

Implementation of results: the obtained results are used in „Ort Braude College of Engineering” in Israel, and can further be used by students and researchers in image processing. Also, an Israeli company (ASI – Applied Spectral Imaging Ltd.) evaluated the PIQ framework and found the benefits of PIQ framework for IQ improvement of images taken by medical spectral imaging system.

Summary


1. ANALYSIS OF SITUATION IN THE SMARTPHONES IMAGE QUALITY
  • 1.1. Image quality assessment background
  • 1.2. The concept of image quality assessment
  • 1.2.1. The image quality circle
  • 1.3. Smartphones vendors in-house image quality assessment
  • 1.4. Image quality attributes
  • 1.4.1. Relationships between image quality attributes
  • 1.4.2. Summary of image quality attributes
  • 1.5. Subjective image quality assessment
  • 1.6. Objective image quality assessment
  • 1.7. The challenge of non-reference image quality assessment
  • 1.8. Research on perceived image quality arena
  • 1.9. Summary

2. PERCEIVED IMAGE QUALITY ASSESSMENT
  • 2.1. Image quality attributes of VIQET
  • 2.2. The image quality assessment method
  • 2.3. Subjective image quality metrics
  • 2.4. HVS-based feature metrics
  • 2.5. Implementation of subjective image quality assessment
  • 2.6. Implementation for perceived image quality prediction
  • 2.7. Human visual test and VIQET image analysis
  • 2.7.1. Subjective IQ assessment for IQAs rating
  • 2.7.2. Extraction of IQAs to VIQET image quality categories
  • 2.7.3. Image quality analysis by VIQET
  • 2.8. Examination of the new model
  • 2.8.1. Image quality valuation with VIQET
  • 2.8.2. The impact of different IQ attributes
  • 2.8.3. Evaluation of smartphone perceived image quality
  • 2.8.4. Perceived image quality based on IQ attributes
  • 2.9. Summary

3. NEW MODEL PERFORMANCE EVALUATION
  • 3.1. VQEG’s standard performance evaluation procedures
  • 3.2. Model evaluation by objective image quality assessment with VIQET
  • 3.2.1. VIQET image quality analysis scores and predicted MOSp
  • 3.3. The new image quality assessment model evaluation
  • 3.4. Model for perceived image quality evaluation
  • 3.5. The study novelty and practical implementation
  • 3.6. Summary

GENERAL CONCLUSIONS AND RECOMMENDATIONS