MIXDES Methods of 3D Images Quality Assesment

Transcription

1 Methods of 3D Images Quality Assesment, Marek Kamiński, Robert Ritter, Rafał Kotas, Paweł Marciniak, Joanna Kupis, Przemysław Sękalski, Andrzej Napieralski LODZ UNIVERSITY OF TECHNOLOGY Faculty of Electrical, Electronic, Computer and Control Engineering Department of Microelectronics and Computer Science 1/13

2 "Registration of Stereoscopic Image" (acronym ROS3D), agreement no. UOD-DEM-1-023/001; under DEMONSTRATOR+ programme and "Hardware and software for automation of 3D filming process"; agreement no. INNOTECH-K3/HI3/16/227587/NCBR/14 under INNOTECH programme" Agenda Introduction Methodology Classification Presentation of chosen measures Conclusion 2/13

3 Introduction The article presents the theme of stereoscopic images quality assessment based on the literature survey. The answer to the question of whether any of the measures is good enough for use in the project. The article does not include presentation of the results of our own analysis. 3/13

4 Methodology Original image Image transformations: - JPEG2000 compression - JPEG compression - white noise - Gaussian blur Disturbed image Result Measures Metrics 4/13

5 Classification (I) Initial available information FR Full Reference assessment methods that are used when the reference image is available, RR Reduced Reference assessment methods that are used when any known reference image elements are available, NR No Reference assessment methods that are used when reference image is not available. 5/13

6 Constructing method Classification (II) Subjective Objective new metrics tailored to the specifics of the 3D image modified (to assess the difference between the left and right image) measures of 2D images evaluation Based on a simple mathematical formula calculation Based on the Human Visual System (HVS) 6/13

7 Subjective measures Difference Mean Opinion Score (DMOS) is a measure used for the subjective assessment of the image. An average experience of the reference and disturbed image. Measurements are taken as a survey on a specific group of people. Assessment of the number of people participating in the study, their selection and possible exclusion criterion remains questionable. The main disadvantage of subjective metrics is its strong dependence on the individual characteristics of the observer and the show conditions. Beside the visual experience of the observer appears: comfort parameter (understood as the absence of effects such as headache or nausea). 7/13

8 Peak Signal-to-Noise Ratio 1 MSE = MN PSNR = 10log I = 2 B 1 M N i= 1 j= 1 B the number of bits per pixel, M, N image size, x ij pixel (i,j) of the original image, x ij pixel (i,j) of disrupted image. 10 x ij 2 I MSE x db ' ij 2 8/13

9 Structural Similarity Index Metric 2μ xμy +C1 2σ xy +C μ + μ +C σ +σ +C SSIM x, y = 2 x y 1 x y 2 Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, 2004, Image quality assessment: From error measurement to structural similarity. IEEE Transactions on Image Processing, 13, /13

10 Visual Signal-to-Noise Ratio 1. Preliminary processing calculate the noise perform discrete wavelet transform of original and noise images in order to obtain a set of sub-bands of wavelet decomposition calculate the spatial frequency vector 2. Evaluation of interference detection for each f m calculate the contrast detection threshold for each f m calculate the actual contrast distortion if the distortion is below the detection limit distorted image is considered excellent (VSNR = ) and the algorithm terminates. 3. Calculate VSNR calculate the perceived contrast distortion d pc calculate the total global contrast distortion d gp calculate VSNR 10/13

11 Color and Sharpness of Edge Distortion Hang Shao, Xun Cao, Guihua Er Broadband, "OBJECTIVE QUALITY ASSESSMENT OF DEPTH IMAGE BASED RENDERING IN 3DTV SYSTEM Networks & Digital Media Laboratory TNList and Department of Automation, Tsinghua University Beijing, P.R.China 11/13

12 Conclusion The problem of stereoscopic images evaluation is still relatively young. Literature reports little opportunity for the use of the 2D indicators. An attempt to help to improve it via attached factor in evaluating the differences in the left and right image also failed to correct the situation. New indicators developed for 3D images, according to the publications, are more suitable to the assessment and the results presented in publications seemed to be promising. However, literature presents only the first tests on a limited: pictures database and number of distortions. 12/13

13 THANK YOU FOR YOUR ATTENTION 13/13