**Blocking Effect or Blockiness**

Most existing no-reference metrics focus on estimating blockiness, which is still relatively easy to detect due to its regular structure, although in practice that is not so easy due to the use of deblocking filters in H.246 and other encoders.

Different techniques are used, such as Wu and Yuen whose research in developing a NR metric based on measuring the horizontal and vertical differences between rows and columns at block boundaries, offers interesting results. Means and standard deviations of the blocks adjacent to each boundary determined masking effect pondering.

On the other hand, Wang et al. model the blocky image as a non-blocky image an then appear the interference with a pure blocky signal. The level of blockiness artifact is detected by evaluating the blocky signal.

Other alternatives are the approach proposed by Baroncini and Pierotti, with the use of multiple filters which extract significant vertical and horizontal edge segments due to blockiness, and also Vlachos proposed an algorithm based on the cross-correlation of subsampled images, and Tan and Ghanbari a metric for blocking detection based in videos compressed in MPEG-2.

#### **Blur**

Another typical artifact defined for no-reference metrics is blurring. It appears in almost all processing phases in communications chain of production. Blurring manifests as a loss of spatial detail in moderate to high spatial activity regions of images. Blurring is directly related to the suppression of the higher order AC DCT coefficients through coarse quantization.

Video Quality Assessment 147

them in front of or behind the viewing screen. Binocular disparity refers to the difference captured by two cameras in computer stereovision. The disparity of an object in a scene depends both on camera baseline and on the distance between the object and the camera. There are different techniques to realize this, such as color or polarization filters, whose intention is to separate the left and right eye views, and orient them to each eye, to produce

The complexity of developing perfect binocular disparity is the cause of introducing impairments and defects on image. We pay our attention to three main factors, namely:

3D stereoscopic dimension carries all the 2-D quality assessment, but in addition some other specific factors must be analyzed to assure the QoE of the final consumer. In this chapter some general aspects to understand 3D video are described in order to justify the

The term 3D denotes stereoscopy, i.e. two-view system used for visualization. Due to the difficulty of creating this type of contents by the use of dual cameras, still a high percentage of stereoscopic video is obtained by the conversion of video from 2D to 3D based on the

The quality is improving with this method, but it is necessary to evaluate the results in depth calculation and the experience of the final user. This aim is of vital importance in 3D

Another feature to consider is the different ways of encoding the views, left and right. As both views are correlated, and present similar content with small differences between them, the techniques for compression take advantage of this feature to reduce the amount of data generated, because it is much higher the amount of data required for broadcasting, and new systems of encoding are necessary to develop its features. It is also important the synchronism, i. e. the methods used in compression are recommendable to assure that both views are seen at the same time with each eye. For this purpose, a series of systems appeared in two groups:

alternatives used in quality assessment and their differences with 2-D video.

quality assessment, at least until the production of all the contests are in real 3D.

extraction of depth information from monoscopic images.

that illusion.

Fig. 14. Description of binocular disparity

**6.2 3D quality systems** 

**Formats and Encoding 3D** 

scene content, camera baseline and screen size

Marziliano et al. worked on blurriness metric. Other metrics have been developed to achieve results while working with other kind of artifacts. Marziliano et al. worked on blurriness metric. As object boundaries are represented by sharp edges, the spreading of significant edges in the image gives a nice approach of blurring. Blurriness and ringing metrics have been developed to evaluate JPEG2000 coding as well.

Other metrics carry out the measure by working with DCT coefficients directly. Coudoux et al. detected the vertical block edges and combined them with several masking models applied in the DCT domain.
