Thesis-NoiseDectection-results/NoiseDectection-metrics/RESULTS.md

NoiseDetection-metrics

Description

All results obtained from NoiseDetection-metrics project in order to classify noisy or not noisy synthesis images obtained from monte carlo rendering process.

Results

1.SV_with_SVM

Description

Study of all SV values obtained from SVD compression of images and trying to classify them (noisy/not noisy image).

Parameters

• Interval chosen from SV (begin and end)
• Number of components from SV
• Kind of normalization (3 differents way)
• Number of zones taken ramdomly in training set from the 16 available of an image
• The features used from pool of features computed from SV

Pool of features

• lab (Use of luminance canal obtained from Lab)
• mscn (Mean Substracted Coeffiecients Normalized)
• low_bits_2 (only 2 bits taken from from lab)
• low_bits_3 (only 3 bits taken from from lab)
• low_bits_4 (only 4 bits taken from from lab)
• low_bits_5 (only 5 bits taken from from lab)
• low_bits_6 (only 6 bits taken from from lab)
• low_bits_4_shifted_2 (only 4 bits shifted by 2 taken from from lab)

2.SV_Statistics

Description

Study of statistics obtained from SV vector obtained from SVD compression.

Computed statistics are:

• Mean
• Median
• Standard deviation
• Variance
• Percentile
• Area under SV curve

Parameters

• Kind of normalization (3 differents way)
• Number of zones taken ramdomly in training set from the 16 available of an image
• The features (here computed statistics) used from pool of features computed from SV

Pool of features

• sub_blocks_stats (all stats)
• sub_blocks_area (only area under curves from sub blocks)
• sub_blocks_stats_reduced
• sub_blocks_area_normed

3.SV_Correlation

Description

Study of correlation (using pearson correlation coefficient) between each components of SV vector on the whole dataset (all scences data).

Parameters

• $n$, the number of highest or lowest correlated components to keep
• Kind of normalization (3 differents way)
• Number of zones taken ramdomly in training set from the 16 available of an image

Pool of features

• lab (Use of luminance canal obtained from Lab)
• mscn (Mean Substracted Coeffiecients Normalized)
• low_bits_2 (only 2 bits taken from from lab)
• low_bits_3 (only 3 bits taken from from lab)
• low_bits_4 (only 4 bits taken from from lab)
• low_bits_5 (only 5 bits taken from from lab)
• low_bits_6 (only 6 bits taken from from lab)
• low_bits_4_shifted_2 (only 4 bits shifted by 2 taken from from lab)

4.MSCN_stats

Description

Study of statistics on MSCN matrix obtained on synthesis image. Only variance is computed on sub blocks.

Parameters

• Kind of normalization (3 differents way)
• Number of zones taken ramdomly in training set from the 16 available of an image
  

Pool of features

Note that mscnvar$n$ is the number of sub blocks.

• mscn_var_4
• mscn_var_16
• mscn_var_64
• mscn_var_16_max (data is sorted from max variance to min)
• mscn_var_64_max (data is sorted from max variance to min)
  

5.Data_separation

Description

Use of different way to train data:

• using all data
• using splited data from human threshold
• using only data close to human threshold

Parameters

• Kind of normalization (3 differents way)
• Number of zones taken ramdomly in training set from the 16 available of an image

Pool of features

• lab (Use of luminance canal obtained from Lab)
• mscn (Mean Substracted Coeffiecients Normalized)
• low_bits_2 (only 2 bits taken from from lab)
• low_bits_3 (only 3 bits taken from from lab)
• low_bits_4 (only 4 bits taken from from lab)
• low_bits_5 (only 5 bits taken from from lab)
• low_bits_6 (only 6 bits taken from from lab)
• low_bits_4_shifted_2 (only 4 bits shifted by 2 taken from from lab)
• ica_diff
• svd_trunc_diff
• ipca_diff
• svd_reconstruct
• highest_sv_std_filters (use of different filters on images and SVD computed on it)
• lowest_sv_std_filters (use of different filters on images and SVD computed on it)