Image engineering is a complex field, primarily due to its diversity and depth. This domain covers various aspects of backend applications for images and graphics, including encoding and decoding technologies, color processing, data compression, and quality assessment. With the continuous advancement of imaging technology, image engineering not only involves efficiently storing and transmitting image data but also maintaining high-quality visuals during processing.
From selecting the color space to applying compression algorithms, each step can impact the final image quality. Understanding the technical details and challenges in image engineering is key to ensuring optimal performance of imaging systems in practical applications.
Assessing Image Quality Through Quantitative Data
In evaluating image quality, objective data serves as the basis for making informed decisions. Common indicators used for image quality assessment include Peak Signal-to-Noise Ratio (PSNR), Video Multimethod Assessment Fusion (VMAF), and Structural Similarity Index (SSIM):
Peak Signal-to-Noise Ratio (PSNR)
Measures the difference between the original image and the compressed image; the higher the value, the better the quality.
Video Multimethod Assessment Fusion (VMAF)
Developed by Netflix, this indicator combines various methods to assess image quality, offering a more accurate reflection of human visual perception.
Structural Similarity Index (SSIM)
Focuses on structural information in images, providing a more accurate representation of visual quality compared to PSNR
By integrating these metrics, we can more accurately assess image quality and select solutions that best meet specific application needs. Whether aiming for the highest image quality or prioritizing resource efficiency, data provides a scientific foundation to ensure every choice withstands scrutiny.
Case Study: Revealing the Truth About Image Quality Through Objective Indicators
We selected a range of capture cards on the market for testing and analysis. Can these capture cards maintain image quality after processing?
First, from a subjective perspective, let’s look at these four images— which one do you think has the best quality?
The results of the analysis are as follows:
PSNR
- Samples 1, 3, and 4 have PSNR values above 30 dB, reaching our benchmark.
- Sample 2’s PSNR falls below the benchmark, indicating poor image quality.
VMAF
- Samples 1, 3, and 4 scored above 80 in VMAF, indicating high visual quality.
- Sample 2’s VMAF score is significantly below the benchmark, further confirming quality issues.
SSIM
- Samples 1, 3, and 4 have SSIM scores close to or above 0.9, showing good structural similarity.
- Sample 2’s SSIM score is far below the benchmark, indicating poor structural similarity.
Summary and Analysis
Sample 1: Although this sample meets the benchmarks for PSNR, VMAF, and SSIM, its performance on dynamic scenes is relatively poor, affecting the overall data
Sample 2: This sample uses a different interface and an interface converter, which transforms an 8-bit signal into a 10-bit interface, resulting in significantly lower image quality and data compared to the other samples. This approach is not recommended.
Sample 3 & 4: Both samples perform exceptionally well, benefiting from high-end image processors that excel in speed and quality of dynamic image processing, far exceeding our standard
The results indicate that high-end image processors offer a clear advantage in maintaining image quality, while interface conversion and inadequate signal processing can significantly impact image quality.
Image Quality Assessment Indicators—Applications and Use Cases
Modern 3C products are increasingly diverse. Image quality assessment tools like PSNR, VMAF, and SSIM can be widely applied across various fields, such as:
- Video conferencing systems
- Video surveillance equipment
- Streaming media services
- Display device quality verification
- Content creation and editing software
- Video codec development
Through quantitative analysis, these indicators help manufacturers and service providers enhance product competitiveness, ensuring users receive the best visual experience
Why Is Image Quality Not as Expected?
Factors that often lead to reduced image quality are typically related to image processing, compression, and display technology. Common factors include:
- Compression Loss: Compression may introduce blocky effects, blurriness, and color distortion.
- Color Processing: Color conversion can cause color deviation or detail loss.
- Resolution Reduction: Lowering resolution makes images blurry and loses detail.
- Noise: Noise can make the image appear cluttered or blurred.
- Computational Precision: Quantization errors in calculations can affect image quality.
- Display Issues: Display color accuracy and settings can impact image appearance.
- Visual Effects: Motion blur and edge effects can harm image clarity.
- Data Transmission Issues: Packet loss or errors during transmission can degrade image quality.
Understanding these quality degradation factors helps make better choices in image processing and transmission to maintain high-quality visuals.
Time to Market with Quality! Allion’s Image Testing and Consulting Services
Allion provides tailored consulting services for resellers, suppliers, and manufacturers to meet different upstream and downstream needs. Through Allion’s verification and consulting services, clients can more efficiently obtain test data analysis and recommendations, accelerating product design, development, and production
Allion’s consulting services team provides:
- Result Analysis: Detailed explanations of verification results to help understand data and identify potential risks.
- Bug and Issue Discussions: Joint discussions on issues found during testing, with recommendations for modifications or solutions.
- Performance Improvement Suggestions: Improvement recommendations based on verification results to optimize product or system performance, security, and reliability.
- Technical Support: Technical assistance to help clients resolve testing issues.
After providing verification and consulting services, Allion can also offer suggestions for product development planning and help formulate industry development strategies based on test results and trends.
Faster
Leveraging our expertise, Allion can significantly shorten testing times for the application ecosystem
Easier
With AI technology and automated testing solutions, we ensure each test is reliable, accurate, and repeatable, streamlining the process and saving time and resources.
Better
With over 30 years of industry experience, a comprehensive testing environment, equipment, and a professional technical team, Allion ensures your product performs optimally in the market.
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