Home > Articles > Hardware

  • Print
  • + Share This
This chapter is from the book

2.6 Image and Video Quality

Video quality may be measured by the quality of experience of viewers, which can usually be reliably measured by subjective methods. There have been many studies to develop objective measures of video quality that correlate well with subjective evaluation results [Cho 14, Bov 13]. However, this is still an active research area. Since analog video is becoming obsolete, we start by defining some visual artifacts related to digital video that are the main cause of loss of quality of experience.

2.6.1 Visual Artifacts

Artifacts are visible distortions in images/videos. We can classify visual artifacts as spatial and temporal artifacts. Spatial artifacts, such as blur, noise, ringing, and blocking, are most disturbing in still images but may also be visible in video. In addition, in video, temporal freeze and skipped frames are important causes of visual disturbance and, hence, loss of quality of experience.

Blur refers to lack or loss of image sharpness (high spatial frequencies). The main causes of blur are insufficient spatial resolution, defocus, and/or motion between camera and the subject. According to the Nyquist sampling theorem, the highest horizontal and vertical spatial frequencies that can be represented is determined by the sampling rate (pixels/cm), which relates to image resolution. Consequently, low-resolution images cannot contain high spatial frequencies and appear blurred. Defocus blur is due to incorrect focus of the camera, which may be due to depth of field. Motion blur is caused by relative movement of the subject and camera while the shutter is open. It may be more noticeable in imaging darker scenes since the shutter has to remain open for longer time.

Image noise refers to low amplitude, high-frequency random fluctuations in the pixel values of recorded images. It is an undesirable by-product of image capture, which can be produced by film grain, photo-electric sensors, and digital camera circuitry, or image compression. It is measured by signal-to-noise ratio. Noise due to electronic fluctuations can be modeled by a white, Gaussian random field, while noise due to LCD sensor imperfections is usually modeled as impulsive (salt-and-pepper) noise. Noise at low-light (signal) levels can be modeled as speckle noise.

Image/video compression also generates noise, known as quantization noise and mosquito noise. Quantization or truncation of the DCT/wavelet transform coefficients results in quantization noise. Mosquito noise is temporal noise, i.e., flickering-like luminance/chrominance fluctuations as a consequence of differences in coding observed in smoothly textured regions or around high contrast edges in consecutive frames of video.

Ringing and blocking artifacts, which are by-products of DCT image/video compression, are also observed in compressed images/video. Ringing refers to oscillations around sharp edges. It is caused by sudden truncation of DCT coefficients due to coarse quantization (also known as the Gibbs effect). DCT is usually taken over 8 × 8 blocks. Coarse quantization of DC coefficients may cause mismatch of image mean over 8 × 8 blocks, which results in visible block boundaries known as blocking artifacts.

Skip frame and freeze frame are the result of video transmission over unreliable channels. They are caused by video packets that are not delivered on time. When video packets are late, there are two options: skip late packets and continue with the next packet, which is delivered on time, or wait (freeze) until the late packets arrive. Skipped frames result in motion jerkiness and discontinuity, while freeze frame refers to complete stopping of action until the video is rebuffered.

Visibility of artifacts is affected by the viewing conditions, as well as the type of image/video content as a result of spatial and temporal-masking effects. For example, spatial-image artifacts that are not visible in full-motion video may be higly objectionable when we freeze frame.

2.6.2 Subjective Quality Assessment

Measurement of subjective video quality can be challenging because many parameters of set-up and viewing conditions, such as room illumination, display type, brightness, contrast, resolution, viewing distance, and the age and educational level of experts, can influence the results. The selection of video content and the duration also affect the results. A typical subjective video quality evaluation procedure consists of the following steps:

  1. Choose video sequences for testing
  2. Choose the test set-up and settings of system to evaluate
  3. Choose a test method (how sequences are presented to experts and how their opinion is collected: DSIS, DSCQS, SSCQE, DSCS)
  4. Invite sufficient number and types of experts (18 or more is recommended)
  5. Carry out testing and calculate the mean expert opinion scores (MOS) for each test set-up

In order to establish meaningful subjective assessment results, some test methods, grading scales, and viewing conditions have been standardized by ITU-T Recommendation BT.500-11 (2002) “Methodology for the subjective assessment of the quality of television pictures.” Some of these test methods are double stimulus where viewers rate the quality or change in quality between two video streams (reference and impaired). Others are single stimulus where viewers rate the quality of just one video stream (the impaired). Examples of the former are the double stimulus impairment scale (DSIS), double stimulus continuous quality scale (DSCQS), and double stimulus comparison scale (DSCS) methods. An example of the latter is the single stimulus continuous quality evaluation (SSCQE) method. In the DSIS method, observers are first presented with an unimpaired reference video, then the same video impaired, and he/she is asked to vote on the second video using an impairment scale (from “impairments are imperceptible” to “impairments are very annoying”). In the DSCQS method, the sequences are again presented in pairs: the reference and impaired. However, observers are not told which one is the reference and are asked to assess the quality of both. In the series of tests, the position of the reference is changed randomly. Different test methodologies have claimed advantages for different cases.

2.6.3 Objective Quality Assessment

The goal of objective image quality assessment is to develop quantitative measures that can automatically predict perceived image quality [Bov 13]. Objective image/video quality metrics are mathematical models or equations whose results are expected to correlate well with subjective assessments. The goodness of an objective video-quality metric can be assessed by computing the correlation between the objective scores and the subjective test results. The most frequently used correlation coefficients are the Pearson linear correlation coefficient, Spearman rank-order correlation coefficient, kurtosis, and the outliers ratio.

Objective metrics are classified as full reference (FR), reduced reference (RR), and no-reference (NR) metrics, based on availability of the original (high-quality) video, which is called the reference. FR metrics compute a function of the difference between every pixel in each frame of the test video and its corresponding pixel in the reference video. They cannot be used to evaluate the quality of the received video, since a reference video is not available at the receiver end. RR metrics extract some features of both videos and compare them to give a quality score. Only some features of the reference video must be sent along with the compressed video in order to evaluate the received video quality at the receiver end. NR metrics assess the quality of a test video without any reference to the original video.

Objective Image/Video Quality Measures

Perhaps the most well-established methodology for FR objective image and video quality evaluation is pixel-by-pixel comparison of image/video with the reference. The peak signal-to-noise ratio (PSNR) measures the logarithm of the ratio of the maximum signal power to the mean square difference (MSE), given by

099equ01.jpg

where the MSE between the test video 099equ02.jpg, which is N1 × N2 pixels and N3 frames long, and reference video s[n1, n2, k] with the same size, can be computed by

Some have claimed that PSNR may not correlate well with the perceived visual quality since it does not take into account many characteristics of the human visual system, such as spatial- and temporal-masking effects. To this effect, many alternative FR metrics have been proposed. They can be classified as those based on structural similarity and those based on human vision models.

The structural similarity index (SSIM) is a structural image similarity based FR metric that aims to measure perceived change in structural information between two N × N luminance blocks x and y, with means μx and μy and variances 099equ04.jpg and 099equ05.jpg, respectively. It is given by [Wan 04]

where σxy is the covariance between windows x and y and c1 and c2 are small constants to avoid division by very small numbers.

Perceptual evaluation of video quality (PEVQ) is a vision-model-based FR metric that analyzes pictures pixel-by-pixel after a temporal alignment (registration) of corresponding frames of reference and test video. PEVQ aims to reflect how human viewers would evaluate video quality based on subjective comparison and outputs mean opinion scores (MOS) in the range from 1 (bad) to 5 (excellent).

VQM is an RR metric that is based on a general model and associated calibration techniques and provides estimates of the overall impressions of subjective video quality [Pin 04]. It combines perceptual effects of video artifacts including blur, noise, blockiness, color distortions, and motion jerkiness into a single metric.

NR metrics can be used for monitoring quality of compressed images/video or video streaming over the Internet. Specific NR metrics have been developed for quantifying such image artifacts as noise, blockiness, and ringing. However, the ability of these metrics to make accurate quality predictions are usually satisfactory only in a limited scope, such as for JPEG/JPEG2000 images.

The International Telecommunications Union (ITU) Video Quality Experts Group (VQEG) standardized some of these metrics, including the PEVQ, SSIM, and VQM, as ITU-T Rec. J.246 (RR) and J.247 (FR) in 2008 and ITU-T Rec. J.341 (FR HD) in 2011. It is perhaps useful to distinguish the performance of these structural similarity and human vision model based metrics on still images and video. It is fair to say these metrics have so far been more successful on still images than video for objective quality assessment.

Objective Quality Measures for Stereoscopic 3D Video

FR metrics for evaluation of 3D image/video quality is technically not possible, since the 3D signal is formed only in the brain. Hence, objective measures based on a stereo pair or video-plus-depth-maps should be considered as RR metrics. It is generally agreed upon that 3D quality of experience is related to at least three factors:

  • Quality of display technology (cross-talk)
  • Quality of content (visual discomfort due to accomodation-vergence conflict)
  • Encoding/transmission distortions/artifacts

In addition to those artifacts discussed in Section 2.6.1, the main factors in 3D video quality of experience are visual discomfort and depth perception. As discussed in Section 2.1.4, visual discomfort is mainly due to the conflict between accommodation and vergence and cross-talk between the left and right views. Human perception of distortions/artifacts in 3D stereo viewing is not fully understood yet. There have been some preliminary works on quantifying visual comfort and depth perception [Uka 08, Sha 13]. An overview of evaluation of stereo and multi-view image/video quality can be found in [Win 13]. There are also some studies evaluating the perceptual quality of symmetrically and asymmetrically encoded stereoscopic videos [Sil 13].

  • + Share This
  • 🔖 Save To Your Account