Video Quality Metrics

What Are Video Quality Metrics?

In order to assess the visual quality of video content, standardized measurements, known as video quality metrics, are used to evaluate various aspects of a video. These metrics assess how well a video preserves its intended visual quality, revealing any distortions introduced during processing or transmission. Video quality is measured either subjectively, through human perception, or objectively, using algorithms to generate quantifiable scores.

Different Types of Video Quality Metrics

Video quality metrics are generally classified into two categories: subjective or objective metrics.

Subjective Metrics

Subjective metrics rely on human viewers to assess video quality. Participants are asked to rate video sequences based on their perceptions, typically using scales or comparative methodologies.

ACS (Absolute Category Rating): A common subjective metric where viewers rate video quality on a scale, often from 1 (poor) to 5 (excellent).
DSIS (Double Stimulus Impairment Scale): Viewers compare a reference video to a distorted video and rate the level of impairment.

Objective Metrics

Objective metrics use algorithms to analyze video quality without human intervention. These metrics provide consistent, repeatable assessments and can be classified further as:

full-reference (usually the most accurate, but requires more compute effort)
reduced-reference (more efficient, but lower accuracy)
no-reference (the least accurate, but most efficient) metrics.

The Video Quality Experts Group (VQEG) works closely with different industries to develop and validate objective video quality metrics. Some examples of these include:

PSNR (Peak Signal-to-Noise Ratio): The most widely used video quality metric, it measures the ratio between the maximum possible power of a signal and the power of corrupting noise. However, this metric doesn’t correlate with actual picture quality.
SSIM (Structural Similarity Index): Evaluates image degradation as a perceived change based on structural information, luminance masking, and contrast masking.
VMAF (Video Multi-method Assessment Fusion): Developed by Netflix, VMAF combines multiple quality metrics with a support vector machine-based regression to provide a single comprehensive score (similar to ACS or mean opinion score for audio quality).

Importance of Video Quality Metrics

With more people watching videos online, high quality is a must. In 2023 alone, over 3 billion people watched, downloaded, or streamed videos on the internet–that’s over 37% of the global population.

Viewer Satisfaction: High-quality video is crucial for maintaining viewer engagement and satisfaction, crucial for subscription and ad-supported platforms.
Content Evaluation: Content creators and distributors use these metrics to evaluate their videos, ensuring they meet audience expectations.
Network Optimization: Video quality metrics help in adjusting compression techniques and streaming protocols, balancing quality and bandwidth utilization.
Quality Control: Broadcasters monitor these metrics to maintain consistency and identify issues like compression artifacts and transmission errors before content reaches viewers.

Benefits and Drawbacks of Video Quality Metrics

Benefits

Consistency: Objective metrics provide repeatable and consistent results, eliminating the variability of human perception.
Efficiency: Automated assessments via objective metrics expedite quality control, allowing for rapid analysis of large volumes of content.
Improvements in Technology: VQMs drive advancements in video encoding and transmission technologies by providing benchmarks for improvements.
Insights and Decisions: Help stakeholders make informed decisions about content delivery, compression settings, and network requirements.

Drawbacks

Subjectivity: Subjective metrics can be inconsistent as opinions vary based on individual perceptions and biases.
Resource Intensive: High-quality subjective assessments require significant resources, including time, participants, and controlled environments.
Complexity: Interpreting and applying certain objective metrics can be complex, requiring expert knowledge and sophisticated tools.
Incompleteness: No single metric can comprehensively capture all aspects of video quality; hence, hybrid approaches or multiple metrics are often needed.

Last Thoughts

Video quality metrics play a crucial role in delivering top-notch video content to audiences worldwide. Using both subjective and objective measurements, creators and businesses can guarantee high-quality videos, thus improving viewer happiness and engagement. Despite inherent challenges in accurately measuring video quality, its benefits significantly outweigh its drawbacks.

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QUICK TIPS

Kimberly Matenchuk

In my experience, here are tips that can help you better assess and optimize video quality using metrics:

Use ensemble models for perceptual scoring
Combine multiple perceptual quality metrics like VMAF, SSIM, and LPIPS through a weighted ensemble approach. This hybrid scoring can better handle edge cases where one metric might misrepresent perceptual quality.
Incorporate bitrate-efficiency analysis
Assess quality not just absolutely, but relative to bitrate. Metrics like BD-Rate can help you understand trade-offs between compression and quality more meaningfully than absolute quality scores alone.
Benchmark against genre-specific content
Different genres (e.g. animation vs. live-action) respond differently to compression and quality algorithms. Maintain genre-based test sets to ensure your metrics capture quality accurately across content types.
Evaluate spatial consistency in perceived quality
Beyond average scores, assess quality uniformity across the frame. Tools that measure spatial variance in perceptual scores help identify regions that are degraded (e.g. noisy backgrounds vs. clean faces).
Track metric confidence intervals
Especially with machine learning-based metrics, report confidence or variance in scores. This provides insight into metric stability and helps diagnose outliers or anomalous evaluations.

Last updated: Apr 30, 2025