dataesr/scientific-paragraphs-categorization huggingface dataset:
Quality assessment of DIBR-synthesized view The evaluation of DIBR views synthesized from uncompressed data using standard image quality metrics has been discussed in literature for still images from FVV in 2D context  [3]  using IRCCyN/IVC DIBR image database. It has been demonstrated that 2D quality metrics originally designed to address image compression distortions are very far to be effective to assess the visual quality of synthesized views. Full-reference objective image quality assessment metrics, VSQA  [12] , and 3DswIM  [13] , have been proposed to improve the performances obtained by standard quality metrics in the evaluation of the DIBR-synthesized images. Both metrics are dedicated to synthesis-related artifacts without compression-related artifacts and both metrics are tested using IRCCyN/IVC DIBR images dataset.
Both measures are a combination of three measures: temporal outliers, temporal inconsistencies, and spatial outliers, using ideal depth. Ideal depth is derived for both no-reference and for full-reference metric for distortion-free rendered video. 3VQM metrics show better performances than PSNR and SSIM using a database of DIBR-generated video sequences. Quality metric proposed in  [17]  is designed for the evaluation of synthesized images which contain artifacts introduced by the rendering process due to depth map errors. It consists of two parts. One part is the calculation of the conventional 2D metric after the consistent object shifts. After shift compensation, the 2D QA model matches the subjective quality score better. The other part is the calculation of the structural score by the Hausdorff distance.
It is based on two quality features which are extracted from both spatial and temporal domains of the synthesized sequence. The first feature focuses on capturing the temporal flicker distortion and the second feature is used to measure the change of the spatiotemporal activity in the synthesized sequence due to blurring and blockiness distortion caused by texture compression. The performances of the proposed metric evaluated on the synthesized video quality database SIAT  [11]  are better than the performances of the commonly used image/video quality assessment methods.
MGA was utilized to decompose images by a series of transforms including wavelet, curvelet, bandelet, contourlet, wavelet-based contourlet, hybrid wavelets, and directional filter banks. MGA can capture the characteristics of image, e. g. , lines, curves, contour of object. IQA based on MGA and IQ metric using Haar wavelet decomposition  [24]  have been evaluated on the database which contains compressed, white noisy, Gaussian-blurred, and fast-fading Rayleigh channel noisy images.
Experimental setup To compare the performances of the image quality measures the following evaluation metrics are used: root mean squared error between the subjective and objective scores (RMSE), Pearson's correlation coefficient with non-linear mapping between the subjective scores and objective measures (PCC) and Spearman's rank order correlation coefficient (SCC). The calculation of DMOS from given MOS and non-linear mapping between the subjective scores and objective measures are done according to test plan for evaluation of video quality models for use with high definition TV content by VQEG HDTV group  [44]. The performances of the metrics MP-PSNR and MW-PSNR are evaluated using two publicly available databases which contain DIBR-synthesized images: the IRCCyN/IVC DIBR image database  [3, 4]  and part of the MCL-3D stereoscopic image database  [5, 6].
The IRCCyN/IVC DIBR image quality database The IRCCyN/IVC DIBR image quality database contains frames from three multi-view video sequences: Book arrival (1024 × 768, 16 cameras with 6. 5 cm spacing), Love-bird1 (1024 × 768, 12 cameras with 3. 5 cm spacing) and Newspaper (1024 × 768, 9 cameras with 5 cm spacing). The selected contents are representative and used by MPEG also. For each sequence four virtual views are generated on the positions corresponding to those positions obtained by the real cameras using seven depth-imagebased rendering algorithms, named A1-A7  [45] [46] [47] [48] [49] [50]. One key frame from each synthesized sequence is randomly chosen for the database. For these key frames subjective assessment in form of mean opinion scores (MOS) is provided. The difference mean opinion scores (DMOS) is calculated as the difference between the reference frame's MOS and the synthesized frame's MOS.
The frames generated by algorithm A7 contain unfilled holes. Due to very noticeable object shifting artifacts in the frames generated by algorithm A1, these frames are excluded from the tests. The focus remains on images synthesized using A2-A7 DIBR algorithms and without registration procedure for alignment of the synthesized and the original frames. The results presented in Sections 4. 2-4. 4 for the IRCCyN/IVC DIBR database are based on the mixed statistics of the DIBR algorithms A2-A7.
The MCL-3D stereoscopic image quality database The part of the stereoscopic image quality database MCL-3D which contains 36 stereopairs generated using four DIBR algorithms and associated mean opinion score (MOS) values is used for testing. These stereoscopic image pairs are rendered from nine image-plus-depth sources: Baloons, Kendo and Lovebird1 of resolution 1024 × 728 and Shark, Microworld, Poznan street, Poznan Hall2, Gt_fly, Undo_dancer of resolution 1920 × 1088. For each source, three views are used for the calculation of the metric score, Fig. 14. Original textures (T1, T2, T3) and their associated depth maps (D1, D2, D3) are obtained by selecting key frames from each of nine multi-view test sequences associated with depth maps. From the middle view (T2, D2), using one of the four DIBR algorithms, the stereoscopic image pair (SL, SR) is generated.
The textures from the outer views, (T1, T3) are used as the reference stereo pair. We have calculated IQA metric score between the DIBR-synthesized stereopair (SL, SR) and the reference stereopair (T1,T3). The score for the stereo pair is calculated as the average of the left and right image scores. In the generation of the MCL-3D database, four DIBR algorithms are used: DIBR with filtering, A1  [45] , DIBR with inpainting, A2  [46] , DIBR without hole-filling, A7 and DIBR with hierarchical hole-filling (HHF), A8  [51]. HHF uses pyramid-like approach to estimate the hole pixels from lower resolution estimates of the 3D wrapped image yielding to the virtual images that are free of any geometric distortions. Adding the depth adaptive preprocessing step before applying the hierarchical hole-filling, the edges and texture around the disoccluded areas can be sharpened and enhanced.
The results presented in sections 4. 2 -4. 4 for the MCL-3D database are based on the mixed statistics of four DIBR algorithms A1, A2, A7, and A8. The original image Shark and the left images from the stereopairs synthesized using four DIBR algorithms (A1, A2, A7, A8) are shown on Fig. 15  from top to bottom and from left to right.
The results are similar with square and round type structuring element, but the computational complexity is significantly lower when the square structuring element is used. Namely, in that case separable pyramid decomposition by rows and columns with downsampling after each step can be easily implemented. In the images from the two chosen databases, straight lines are dominant and squared-shaped structuring element is chosen. Moreover, the impact of structuring element size used in morphological operations and the number of decomposition levels in MBP ED pyramid decompositions on MP-PSNR performances is investigated. MP-PSNR performances are calculated using MBP ED pyramid decomposition with different number of decomposition levels (1-7 for IRCCyN/IVC DIBR database and 1-8 for MCL-3D database) and with square structuring elements of different sizes from 2 × 2 to 13 × 13.
More features are removed from the image at each decomposition level as larger structuring element is used. The number of decomposition levels for the best MP-PSNR performances depends on the size of structuring element. The performances of MP-PSNR using SE of different sizes and the best number of decomposition levels for that size of SE are shown in the upper part of Table  1. For the IRCCyN/IVC DIBR database, the MP-PSNR performances show improvement with enlargement of the structuring element. The MP-PSNR performances are noticable better for SE of size 5 × 5 and higher. Matlab implementation of MP-PSNR is available online  [52]. In the case of MCL-3D database, the operation sum is used in the calculation of MP-MSE (9) as better performances of MP-PSNR are achieved. For the For the comparison with linear case, the image decomposition is performed using Laplacian pyramid with linear filters.
Simple and efficient binomial filters  [53]  as approximation of a Gaussian filters are used. Binomial filters' coefficients are from Pascal's triangle, normalized with their sum. Two-dimensional filter is implemented as cascade of one-dimensional filters. The MP-PSNR performances using pyramid decompositions with linear filters are similar for all filter lengths. For the IRCCyN/IVC DIBR database, Pearson's correlation varies from 0. 771 for the linear filter of length 2 to 0. 799 for the linear filter of length 13. For the MCL-3D database, Pearson's correlation varies from 0. 322 for the linear filter of length 2 to 0. 377 for the linear filter of length 3. Pearson's correlation coefficients of MP-PSNR versus DMOS for different filter lengths used in linear pyramid decomposition and for different sizes of SE used in morphological pyramid decomposition are shown on Fig.
18 , left for the IRCCyN/IVC DIBR database and right for the MCL-3D database. The results on Fig. 18  are based on the mixed statistics of the DIBR algorithms A2-A7 for the IRCCyN/IVC DIBR database and A1, A2, A7, A8 for the MCL-3D database. MP-PSNR using pyramid decomposition with morphological filters has much better performances than MP-PSNR using pyramid decomposition with linear filters.
Also, PSNR performances at middle and higher pyramid scales are much better than the PSNR performances for the case when the PSNR is calculated between the original and the DIBR-synthesized images without decomposition, in the base band. The best PSNR performances by pyramid images for different sizes of SE used in morphological pyramid decomposition are shown in Table  2. For the morphological pyramid decomposition using SE of size 3 × 3, the best PSNR performances are achieved for the detail image at pyramid level 5, Pearson correlation coefficient 0. 89 and Spearman correlation coefficient 0. 867. For the MCL-3D database, Pearson's correlation coefficients of PSNR versus MOS for pyramid images at all pyramid scales using structuring elements of different sizes are shown on Fig. 20. For this database, smaller differences between PCC for pyramid images at different scales exist.
The smallest PCC is at the first scale (detail images d 0 ) and the highest PCC is for the aproximation images at the highest scale. The best pyramid image PSNR performances for different sizes of SE used in morphological pyramid decomposition are shown in Table  2. For both databases,it is shown that PSNR shows very good agreement with human quality judgments when it is calculated at higher scales of MBP ED pyramid, much better than for the full resolution images in the base band. Matlab implementation of PSNR by morphological pyramid images is available online  [52].
The performances of the reduced version of MP-PSNR Based on the results of PSNR performances calculated separately by pyramid scales, we propose reduced version of MP-PSNR using only pyramid images with higher PCC values of PSNR towards subjective scores. Reduced  version of MP_MSE is calculated as the weighted sum of the used subbands' MSE  (9). For the IRCCyN/IVCDIBR database, the reduced version of MP-PSNR is calculated using only three detail images with higher PCC values of PSNR towards DMOS. The performances of the reduced versions of MP-PSNR using equal value weights are presented in the bottom left part of Table  1. Reduced version of MP-PSNR has better performances than its full version: from 1. 74 % when the MBP ED pyramid decomposition with SE of size 11 × 11 is used to 6. 75 % when the MBP ED pyramid decomposition with SE of size 3 × 3 is used.
21. Each point represents one frame from the database. Matlab implementation of reduced version of MP-PSNR is available online  [52]. For the MCL-3D database, the reduced version of MP-PSNR is calculated without detail images from the first three pyramid scales when the SE of size less than 7 × 7 is used. When the SE of size 7 × 7 and bigger is used, only the pyramid image from the first scale is omitted in the calculation of the reduced version of MP-PSNR. The performances of the reduced versions of MP-PSNR using equal value weights are presented in the bottom right part of Table  1. Only marginal improvement is achieved using reduced version of MP-PSNR for MCL-3D database.
For the comparison with linear wavelet decompositions, MW-PSNR performances are calculated using separable linear wavelet decompositions using Haar wavelet (Haar) and Cohen-Daubechies-Feauveau wavelet cdf(2,2) and non-separable linear wavelet decomposition with quincunx sampling using cdf(2,2)Q. PSNR performances calculated by wavelet subbands through decomposition scales are investigated. The reduced version of MW-PSNR using only wavelet subbands with better PSNR performances is analyzed. The number of decomposition levels has been varied between 1 and 8 and the configurations with the best MW-PSNR performances have been chosen. The best MW-PSNR performances have been achieved using separable wavelet transformations in M = 7 levels producing 22 subbands. Using non-separable wavelet transformation with quincunx sampling for the IRCCyN/IVC DIBR database, the best MW-PSNR performances have been achieved also with M = 7 levels producing 15 subbands.
For the MCL-3D database the best MW-PSNR performances using nonseparable wavelet transformation have been achieved with M = 4 levels producing nine subbands. Equal value weights are used in the calculation of MW-MSE  (11). Matlab implementation of MW-PSNR is available online  [55]. The performances of MW-PSNR for different wavelet transformations are presented in the upper part of Table  3. The performances of MW-PSNR using morphological wavelet transforms are better than the performances of MW-PSNR using linear wavelet transforms. The best MW-PSNR performances have been obtained.
Analysis of PSNR performances by wavelet subbands We have investigated PSNR performances by wavelet subbands at different wavelet decomposition scales. The reference image and the DIBR-synthesized image are decomposed into a sets of lower resolution subbands using morphological wavelet decomposition. At each decomposition scale, for each wavelet subband, PSNR is calculated between the subbands of the two wavelet representations, the reference image wavelet representation and the DIBR-synthesized image wavelet representation. Pearson's correlation coefficient (PCC) of PSNR to subjective scores is calculated for each subband for three types of morphological wavelets: minHaar, minLift and minLiftQ. Matlab implementation of PSNR by morphological wavelet subbands is available online  [55]. For the IRCCyN/IVC DIBR database, Fig.
23 , Pearson's correlation coefficients calculated for wavelet subbands on decomposition levels 4-7 are higher than Pearson's correlation coefficients calculated for wavelet subbands on decomposition levels 1-3. For the MCL-3D database, smaller differences by wavelet subbands between Pearson's correlation coefficients can be noticed, Fig. 24. Moreover, the best PSNR performances by wavelet subbands for each wavelet decomposition are shown in Table  4. For instance, for the IRCCyN/IVC DIBR database for the separable wavelet decomposition using morphological minLift wavelet, the best PSNR performances are obtained for subband on the scale 6 with vertical details (d61), PCC 0. 887 and SCC 0. 828. Also, for all tested wavelets, the PSNR of the wavelet subband with the highest PCC show much better performances than PSNR calculated between the reference image and the DIBR-synthesized image without decomposition in the base band.
Analysis of the reduced version MW-PSNR performances Based on the PSNR performances by subbands for the IRCCyN/IVC DIBR database given in Fig. 23 , it can be concluded that the PSNR performances of wavelet subbands at decomposition levels 4-7 are much better than the subband PSNR performances on levels 1-3. Therefore, we propose reduced version of MW-PSNR using only these higher level subbands. Reduced versions of MW_MSE is calculated as weighted sum of the used subbands' MSE. For the separable wavelet decomposition, the reduced version of MW-PSNR is calculated using only 11 subbands from levels 4-7 with indices 41-72. For the nonseparable wavelet decomposition with quincunx sampling, reduced version of MW-PSNR is calculated using 6 subbands from decomposition levels 4-7 with indices 42-71.
Summary of the results The performances of the selected proposed metrics, the commonly used 2D image quality assessment metrics and the metric dedicated to synthesis-related artifacts, 3DswIM  [13] , are presented in Table  5. The considered commonly used 2D metrics are: PSNR, universal quality index UQI  [56] , structural similarity index SSIM  [57] ,  multi-scale structural similarity MS-SSIM  [19] , information weighted IW-PSNR  [20] , and IW-SSIM  [20]. Singlescale structural similarity SSIM  [57]  is calculated between the original and the synthesized images using the given matlab code  [58]. 3DswIM  [13]  is calculated using the given matlab p-code  [59]. Selected versions of the proposed metrics using morphological pyramid decompositions presented in Table  5  are: PSNR calculated on scale 5 of the MBP ED pyramid representations using SE of size 3 × 3; reduced version of MP-PSNR using SE of size 5 × 5 in pyramid MBP ED decomposition; full versions of MP-PSNR using SE of size 5 × 5.
The selected proposed metrics using morphological wavelet decompositions shown in Table  5  are: PSNR calculated on scale 6 between wavelet subbands with vertical details of the two wavelet representations using minLift wavelet for the IRCCyN/IVC DIBR database and PSNR calculated on scale 7 between approximation wavelet subbands using minLift wavelet for the MCL-3D database; reduced and full versions of MW-PSNR using minHaar wavelet. The performances of the proposed metrics are much better than the performances of the commonly used 2D metrics and better than the performances of the metric dedicated to synthesis-related artifacts, 3DswIM. The Pearson's correlation coefficients of the selected commonly used 2D metrics, the metric dedicated to synthesisrelated artifacts, 3DswIM, and the reduced versions of MP-PSNR and of MW-PSNR are shown on Fig.
We have explored two versions of morphological multi-scale metric, Morphological Pyramid Peak Signal-to-Noise Ratio measure, MP-PSNR, based on morphological pyramid decomposition and Morphological Wavelet Peak Signal-to-Noise Ratio measure, MW-PSNR, based on morphological wavelet decomposition. The proposed metrics are evaluated using two databases which contain images synthesized by DIBR algorithms: IRCCyN/ IVC DIBR image database and MCL-3D stereoscopic image database. Both metric versions demonstrate high improvement of performances over standard IQA metrics and over tested metric dedicated to synthesis-related artifacts. Also, they have much better performances than their linear counterparts for the evaluation of DIBR-synthesized It is shown that PSNR has particularly good agreement with human judgment when it is calculated between the appropriate detail images at higher decomposition scales of the two morphological multi-scale image representations.
For IRCCyN/IVC DIBR images database, PSNR calculated on scale 5 of the MBP ED pyramid image representations using structuring element of size 3 × 3 has very good performances, Pearson's 0. 89 and Spearman 0. 86. For MCL-3D database, PSNR calculated on scale 4 of the MBP ED pyramid image representations using square structuring element of size 7 × 7 achieves Pearson's 0. 88, Spearman 0. 82. For IRCCyN/IVC DIBR images database, it has been shown that reduced versions of multi-scale metrics, reduced MP-PSNR and reduced MW-PSNR, can be used for the assessment of DIBR-synthesized frames with high reliability. Reduced version of MP-PSNR using morphological pyramid decomposition MBP ED with square structuring element of size 5 × 5 achieves the improvement 15. 2 % of correlation over PSNR (Pearson's 0.
The best performances of PSNR by wavelet subbands for each wavelet Fig. 25The IRCCyN/IVC DIBR database: Fitted scores of reduced MW-PSNR versus MOS. Reduced version of MW-PSNR is based on wavelet subbands from decomposition levels 4-7; morphological wavelet decomposition using minHaar wavelet in seven levels is used images. MP-PSNR has slightly better performances than MW-PSNR. For the MCL-3D database, MP-PSNR achieves Pearson 0. 888 and Spearman 0. 756 using MBP ED pyramid decomposition with square structuring element of size 7 × 7 in 4 levels. For the same database, MW-PSNR achieves Pearson 0. 87 and Spearman 0. 707 using separable wavelet decomposition with morphological Haar wavelet in 7 levels.
ACKNOWLEDGMENT The authors wish to express special thanks to  Prof. Motoichi Ohtsu  ( University of Tokyo ) and  Prof. Itsuki Bannno  ( Yamanashi University ) for active support and discussion. We thank  Mrs. Etsuko Ota  ( University of Tokyo ) for the AFM measurements. This work was partially supported by a  MEXT   Grant-in-Aid for Scientific Research (B)  (No. 26286022 ,  25288012 ), a  MEXT Grant-in-Aid for Exploratory Research Program  (No. 26630122 ), a  MEXT Grant-in-Aid for Scientific Research on Innovative Areas  (No. 15H00866 ), a  MEXT Nanotechnology Platform  (No. 12024046 ), the  JSPS Core-to-Core Program (A. Advanced Research Networks) , the  Yazaki Memorial Foundation , the  Research Foundation for Opto-Science and Technology , and the  RIKEN AICS  through the  HPCI System Research project  (ID: hp150218).
(  1 ) and (  2 ) using standard Runge-Kutta functions coded in MATLAB. The parameters of the vocal folds and the vocal tract were set at values described elsewhere  (Assaneo and Trevisan, 2013; Lucero and Koenig, 2005)  except for the timedependent parameters of subglottal pressure P s , vocal fold tension Q and vocal tract shape q 1 , q 2 and c k (i) (Eqs. (  1 ) and (  2 ) in bold type) that were varied to construct the different audio samples. Following  (Story, 2005) , the vocal tract was discretized in N = 44 tubes. The phonetic content of the synthetic sounds was the same as the real samples, built as follows: the five Spanish vowels /a/, /e/, /i/, /o/ and /u/ were synthesized using a vocal tract of coefficients q 1 and q 2 of Eq. (  2 ) at constant values (see  Assaneo et al. , 2013).
The three types of audio stimuli were set to the same overall energy (RMS) and presented to the participants with a white noise baseline of 2% of the maximal intensity. In the fMRI experiments, the auditory stimuli were played binaurally at a mean 90 dB sound pressure, using foam earplugs and noiseattenuated MRI-compatible headphones (Resonance Technology Inc. ). In behavioral tasks performed outside the scanner, mono audio files at a sampling rate of 44. 1 kHz were presented to the participants via headphones Logitech B530 USB Headset MS Linc Optimi. The experiments were written in MATLAB, using the Psychophysics Toolbox extensions  (Brainard, 1997).