**Ringing**

Ringing artifacts are fundamentally related to the Gibb's phenomenon, when quantization of individual coefficients results in high-frequency irregularities of the reconstructed block. Yu et al. offers a segmentation algorithm to identify regions with ringing artifacts, which is more evident along high contrasts in regions with smooth texture and complex texture. The original and the processed video sequences are input into the metric and decomposed respectively by the spatial-temporal filter banks

Based on structural similarity

The methods traditionally for quality assessment attempted to quantify the visibility of differences between pairs of images, a distorted image and ist corresponding reference image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, some researchers introduce alternative studies for quality assessment based on the degradation of structural information.

#### **SSIM**

The most representative metric based on Structural Similarity is SSIM. The metric proposed by Wang et al. is based on the combination of three properties of the image luminance, contrast and structure, by comparison between the original and the impaired image, three conditions must be met: symmetry, boundedness and being unique maximum.

$$SSIM(\mathbf{x}, \mathbf{y}) = [l(\mathbf{x}, \mathbf{y})]^\alpha \cdot [c(\mathbf{x}, \mathbf{y})]^\beta \cdot [s(\mathbf{x}, \mathbf{y})]^\gamma$$

### **VQM**

Xiao, F. proposed a modified DCT-based video quality metric (VQM) based on Watson's proposal, which exploits the properties of visual perception, using the existing DCT coefficients, so it only incurs slightly more computation overhead.

Based on vision models

There are a wide range of systems which utilizes models that attempt to reproduce similarities with the Human Visual System. In this section, four of them are introduced.


magnitude between the original and the impaired image. SI is the root mean square of the

1 1 1 1

 

*F C F C*

*i j i j*

Ringing artifacts are fundamentally related to the Gibb's phenomenon, when quantization of individual coefficients results in high-frequency irregularities of the reconstructed block. Yu et al. offers a segmentation algorithm to identify regions with ringing artifacts, which is more evident along high contrasts in regions with smooth texture and complex texture. The original and the processed video sequences are input into the metric and decomposed

The methods traditionally for quality assessment attempted to quantify the visibility of differences between pairs of images, a distorted image and ist corresponding reference image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, some researchers introduce alternative studies for quality

The most representative metric based on Structural Similarity is SSIM. The metric proposed by Wang et al. is based on the combination of three properties of the image luminance, contrast and structure, by comparison between the original and the impaired image, three

> *SSIM x y l x y c x y s x y* ( , ) [ ( , )] [ ( , )] [ ( , )]

Xiao, F. proposed a modified DCT-based video quality metric (VQM) based on Watson's proposal, which exploits the properties of visual perception, using the existing DCT

There are a wide range of systems which utilizes models that attempt to reproduce similarities with the Human Visual System. In this section, four of them are introduced.

 Just Noticeable Differences (JND). The Sarnoff Model also known as Visual Discrimination Model (VDM) was copyrighted by Tektronix Company and commercialized in their PQA600 Picture Analyzer. The model works in spatial domain. Its acceptable conclusions are obtained as a result of its high fidelity in

 Visual Differences Predictor (VDP). Unlike JND, VDP works in frequency domain, and it is very popular in prediction of encoding errors thanks to the labour of S.

 

conditions must be met: symmetry, boundedness and being unique maximum.

0 0 0 0

2 2

*ref impaired*

spatial gradient (SG), so blurring is computed as follows:

respectively by the spatial-temporal filter banks

assessment based on the degradation of structural information.

coefficients, so it only incurs slightly more computation overhead.

comparison with HVS, due to its complexity.

Based on structural similarity

**Ringing** 

**SSIM** 

**VQM** 

Based on vision models

<sup>1</sup> ( ) (, )

*BL x SG i j SG FxC*

Daly. The model is based on the comparison of two images after creating a diagram of disparity, to detect the image variation.

