The document discusses quality prediction of asymmetrically distorted stereoscopic images. Existing methods generate substantial prediction bias when applied to asymmetrically distorted images. The paper builds a database of single-view and symmetrically/asymmetrically distorted stereoscopic images. A subjective test found prediction bias could be overestimation or underestimation depending on distortion type/level. Eye dominance was found to not strongly impact quality decisions. A new model using information content and divisive normalization pooling, inspired by binocular rivalry, successfully eliminates prediction bias, improving quality prediction of stereoscopic images.

1. Quality Prediction of Asymmetrically Distorted
Abstract:
Objective quality assessment of distorted stereoscopic images is a challenging problem,
especially when the distortions in the left and right views are asymmetric. Existing studies
suggest that simply averaging the quality of the left and right views well predicts the quality of
symmetrically distorted stereoscopic images, but generates substantial prediction bias when
applied to asymmetrically distorted stereoscopic images. In this paper, we first build a database
that contains both single-view and symmetrically and asymmetrically distorted stereoscopic
images. We then carry out a subjective test, where we find that the quality prediction bias of the
asymmetrically distorted images could lean toward opposite directions (overestimate or
underestimate), depending on the distortion types and levels. Our subjective test also suggests
that eye dominance effect does not have strong impact on the visual quality decisions of
stereoscopic images. Furthermore, we develop an information content and divisive
normalization-based pooling scheme that improves upon structural similarity in estimating the
quality of single-view images. Finally, we propose a binocular rivalry inspired multi-scale model
to predict the quality of stereoscopic images from that of the single-view images. Our results
show that the proposed model, without explicitly identifying image distortion types, successfully
eliminates the prediction bias, leading to significantly improved quality prediction of the
stereoscopic images.
Existing System:
Objective quality assessment of distorted stereoscopic images is a challenging problem,
especially when the distortions in the left and right views are asymmetric. Existing studies
suggest that simply averaging the quality of the left and right views well predicts the quality of
symmetrically distorted stereoscopic images, but generates substantial prediction bias when
applied to asymmetrically distorted stereoscopic images.

2. Disadvantages of existing system :
.Generates substantial prediction bias when applied to asymmetrically distorted
stereoscopic images eye dominance effect
.Does not has strong impact on the visual quality decisions of
Stereoscopic images.
.Successfully eliminates the prediction bias, leading
to significantly improved quality prediction of the stereoscopic
Advantages of proposed system:
.Generatessubstantialpredictionbiaswhenappliedtoasymmetricaldistorts
ereoscopic images Eye dominance effect.
.Does not has strong impact on the visual quality decisions of Stereoscopic
images
.Successfully eliminates the prediction bias, leading to significantly improved
quality prediction of the stereoscopic

3. Proposed System:
In this paper, we first build a database that contains both single-view and symmetrically
and asymmetrically distorted stereoscopic images. We then carry out a subjective test, where we
find that the quality prediction bias of the asymmetrically distorted images could lean toward
opposite directions (overestimate or underestimate), depending on the distortion types and levels.
Our subjective test also suggests that eye dominance effect does not have strong impact on the
visual quality decisions of stereoscopic images. Furthermore, we develop an information content
and divisive normalization-based pooling scheme that improves upon structural similarity in
estimating the quality of single-view images. Finally, we propose a binocular rivalry inspired
multi-scale model to predict the quality of stereoscopic images from that of the single-view
images.
SOFTWARE AND HARDWARE REQUIREMENTS:
SYSTEM REQUIREMENTS:
 Operating system : Windows XP/7.
 Coding Language : MATLAB
 Tool : MATLAB R 2012
HARDWARE REQUIREMENTS:
 System : Pentium IV 2.4 GHz.
 Hard Disk : 40 GB.
 Floppy Drive : 1.44 Mb.
 Monitor : 15 VGA Colour.
 Mouse : Logitech.
Ram : 512 Mb.