1) The document presents a comparison of tone-mapping algorithms based on structural fidelity and statistical naturalness. It proposes a quality assessment algorithm that combines a structural similarity index with naturalness measures of intensity statistics. 2) Tone-mapping operators are used to convert high dynamic range images to low dynamic range for display on standard monitors. The proposed method assesses quality by analyzing both structural fidelity and naturalness. 3) Adaptive fusion tone-mapping is used to remove losses and display high quality HDR images on LDR displays. Structural fidelity and statistical naturalness are calculated and parameters are automatically tuned based on thresholds.

1. PAAVAI COLLEGE OF ENGINEERING
COMPARISON OF TONE-MAPPING ALGORITHM BASED ON
STRUCTURAL FIDELITY AND STATISTICAL NATURALNESS
PRESENTED BY:
JUVVALAPALEM SUMADEEP
R.G.GOWTHAM
R.KARTHICK
GUIDED BY:
R.KARTHICK..,ASST PROF

2. OBJECTIVES
• The objective of this project is to allow the
HDR images on LDR visual displays. Here we
propose an objective quality assessment
algorithm for tone- mapped images by
combining: 1) a multi scale signal fidelity
measure on the basis of a modified structural
similarity index and 2) a naturalness measure
on the basis of intensity statistics of natural
images.

3. ABSTRACT
Tone-mapping operators[TMOs] that converting high
dynamic range[HDR] to low dynamic range[LDR]
images practically useful tools for the visualization of
HDR images on standard LDR displays by comparing
statistical naturalness and structural fidelity.

4. LITERATURE SURVEY
RECOVER REAL WORLD SCENE
OBJECTIVES:
• The objective of this project is a new method of high quality image in painting
for recovering original scene of degraded image using reference images of
different exposures.
TOOLS:
• Inter-pixel relationship function , Dual patching.
DEMERITS:
• Useful data can be lost due to camera shake , especially when capturing by a
hand device , which generates noticeable artefact in the synthesized image.

5. FAST MOVEMENT DETECTION FOR HDR
IMAGING
OBJECTIVES:
 The objective of this project is improving a quality and speed of
final HDR images .
TOOLS:
 Sub sampling method , new adaptive threshold , new patching .
DEMERITS:
 If a pixel is detected as a background one , its neighboring pixels
are skipped .

6. COMPARISION
EXISTING SYSTEM
 HDR images doesn’t
clearly visible on standard
LDR displays.
 Losses will occurred while
allowing HDR image on
LDR displays.
 It is expensive and time
consuming.
 Manual parameter tuning.
 Image quality is poor
PROPOSED
SYSTEM
 HDR images are allowed to
visible on standard LDR
displays.
 Losses will removed using
adaptive fusion tone-
mapping technique
 Cheaper
 Automatic parameter
tuning.
 Image quality is high

7. PROPOSED BLOCK DIAGRAM
LDR image created by TMO or
photoshop
LDR image created by TMO or
photoshop
Decomposition Laplacian
pyramid transform
Decomposition Laplacian
pyramid transform
Scale1 Fused Scale1
Scale2 Fused Scale2
Scale3 Fused Scale3
Scale4 Fused Scale4
Reconstruction

8. ALGORITHM
 Take an HDR image
 Convert HDR image in to LDR image using TMO’s or
Photoshop
 Compare the quantity, local intensity and naturalness of
two images
 Take an high quality image from the compared images
 Choose the high parameter image that was above the
threshold
 If the image parameter is below the threshold then it will
repeated the same process
 The HDR image will displayed in LDR displays

9. FLOW CHART
LDR image created by TMO or
photoshop
Structural
fidelity
Statistical
naturalness
Quality assessment
Parameters are above
threshold
Output image
Parameter tuning

10. MODULE PHASE 1
QUALITY ASSESSMENT METHOD OF TONE-
MAPPING OPERATORS:
 Tone-mapping operators that convert high dynamic range image to
low dynamic range and allow to make visible on LDR displays.
 quality assessment method that defines to achieve a good
comparison between structural fidelity and structural naturalness.
 The algorithms using in this are:
 Structural fidelity
 statistical naturalness

11. STRUCTURAL FIDELITY
 It compare luminance, contrast & structure of LDR & HDR.
 It extract the information about image structure.
 It based on two intuitive
 signal strength is significant
 signal strength is insignificant
 Estimated value applied in sliding window technique for
obtain structure of image.

12. STATISTICAL NATURALNESS
 Important for image processing application &
biological vision
 A high quality tone mapped LDR image should
not only faithfully preserve the structural
fidelity of the HDR image but also look natural
 It discuss about the naturalness of the image
quantity.

14. MODULE PHASE 2
PARAMETER TUNING
 The parameter tuning automatically adjust the
parameter values of the image which can be done
by using TMQI.
 It consists two coefficient values SRCC and
KRCC.
 These values compare with threshold value
and dynamic range of the image automatically

15. Adaptive fusion
 Adaptive fusion technique which is used for combine
many TMO images and objective quality measure.
 It can be done by using Discrete wavelet transform.
Wavelets work for decomposing signals (such as
images) into hierarchy of increasing resolutions: as we
consider more layers, we get more and more detailed
look at the image.
 At first laplacian pyramid transform decompose image
into different scales and find the coefficient value.
 Then the fusion technique applied for an multiple co-
efficient, the image to be reconstruct.

17. REFERENCE’S
• Eric c. Larson, Damon M. Chandler, “Most apparent
distortion: full-reference image quality assessment and the
role of strategy”, journal of electronic imaging 19(1),
011006, Mar 2010.
• Fredo Durand, Julie Dorsey,” Fast Bilateral imaging for
the recovery of High Dynamic Range imaging”,
Massachusetts Institute of Technology,2002.
• Hojatollah Yeganeh, “Objective Quality Assessment of
Tone-Mapped Images”, student Member, IEEE, and Zhou
Wang, Member, IEEE Transactions on image
processing,VOL.22, NO.2, Feb 2013.
• Z. J. Zhu, Z.G. Li, S. Rahardja, and P. Franti, “Recover
real world scene: high quality image in painting using
multi-exposed references”, Electronics Letters, PP.1310-
1312, VOL. 45, Issue 25, Dec 2009.