A Completely Blind Video Integrity Oracle

ABSTRACT:

Considerable progress has been made toward developing still picture perceptual quality analyzers that do not require any reference picture and that are not trained on human opinion scores of distorted images. However, there do not yet exist any such completely blind video quality assessment (VQA) models. Here, we attempt to bridge this gap by developing a new VQA model called the video intrinsic integrity and distortion evaluation oracle (VIIDEO). The new model does not require the use of any additional information other than the video being quality evaluated. VIIDEO embodies models of intrinsic statistical regularities that are observed in natural videos, which are used to quantify disturbances introduced due to distortions. An algorithm derived from the VIIDEO model is thereby able to predict the quality of distorted videos without any external knowledge about the pristine source, anticipated distortions, or human judgments of video quality. Even with such a paucity of information, we are able to show that the VIIDEO algorithm performs much better than the legacy full reference quality measure MSE on the LIVE VQA database and delivers performance comparable with a leading human judgment trained blind VQA model. We believe that the VIIDEO algorithm is a significant step toward making real-time monitoring of completely blind video quality possible.

PROJECT OUTPUT VIDEO:

EXISTING SYSTEM:

• In the past we have proposed the use of exemplar natural pictures to serve as ground truth relative to which statistical regularities may be modeled. Such an approach, although much more general than existing blind IQA models, is still limited in that it can only capture the common baseline characteristics of a specific collection of non-distorted content, and therefore may fail to represent some video specific intrinsic characteristics.

• Also, the construction of such a database requires the unbiased selection and maintenance of hundreds of natural undistorted videos. This also raises the question of how many exemplar videos are needed to design an accurate natural video model, and how diverse and distinctive these need to be relative to each other and to the world of videos.

DISADVANTAGES OF EXISTING SYSTEM:

• Finally, given current limitations of image/video camera capture, distortions are inevitably introduced in the acquisition process and hence the procurement of perfectly natural ‘pristine’ videos is practically impossible.

PROPOSED SYSTEM:

• In this article, we explain our ‘quality aware’ natural video statistics model in the space-time domain and describe the relevant temporal features that are derived from it and used to model inter subband correlations over local and global time spans.

• The overall model, which we call VIIDEO, is the basis of a VIIDEO algorithm that predicts video quality in a manner that correlates quite well with human judgments of video quality. We compare the performance of VIIDEO against existing state-of-the-art FR and NR VQA approaches.

ADVANTAGES OF PROPOSED SYSTEM:

• VIIDEO algorithm performs much better than the legacy full reference quality measure MSE on the LIVE VQA database and delivers performance comparable with a leading human judgment trained blind VQA model.

• We believe that the VIIDEO algorithm is a significant step toward making real-time monitoring of completely blind video quality possible.

SYSTEM REQUIREMENTS:

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.

SOFTWARE REQUIREMENTS:

• Operating system : Windows XP/7.

• Coding Language : MATLAB

• Tool                      : MATLAB R2013A

REFERENCE:

Anish Mittal, Michele A. Saad, and Alan C. Bovik, Fellow, IEEE, “A Completely Blind Video Integrity Oracle”, IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 25, NO. 1, JANUARY 2016.