**Use a Unified Video Standard Source for Testing**
After Emperor Qin Shihuang unified the country, he standardized language, weights, and measures, which greatly boosted economic and social development. Today, hard disk recorders (DVRs) are in a state similar to the Spring and Autumn and Warring States periods—each with its own compression methods, leading to a chaotic landscape. The image quality varies widely, and without a unified video test signal source, it’s impossible to fairly compare performance. DVRs receive video signals from cameras and output them to monitors. To ensure a fair comparison, all devices should use the same standard test signal. This simplifies the evaluation process and ensures consistency across different systems.
**Test System Framework**
The testing system includes a computer monitor, video analysis software, VGA monitors for comparing static standard video signals, multiple DVRs, video signal sources, visual comparison tools, composite analyzers, and monitors A and B. It also involves standard video signals, audio signals, motion images, DVD players, video test pattern generators, cameras, and computer monitors. The system aims to evaluate both static and dynamic video signals, ensuring accurate comparisons between original and compressed content.
**Moving Image Compression Efficiency Evaluation**
Since DVRs handle a wide range of moving images, evaluating compressed video is more complex than static images. Factors like image quality, data rate, image complexity, and encoding algorithms all affect performance. Static test signals cannot fully capture real-world scenarios, so actual dynamic scenes are needed. When moving images are digitized and compressed, they often suffer from distortion or damage. Choosing the right moving image is crucial—it should avoid story-driven content that distracts viewers and instead include varied details, brightness, contrast, and color levels.
The selected moving images should have diverse characteristics: varying object sizes in the background, different movement directions (straight, curved, irregular), basic colors (red, green, blue, yellow, white, black), changing speeds, camera movements (zooming, panning), and complex or simple backgrounds. These features represent typical motion patterns, helping to assess how well different compression techniques perform.
Based on these characteristics, modern video coding systems typically consist of two parts: traditional texture and motion information coding (like motion compensation), and specific shape coding in standards such as MPEG-4.
Different compression methods produce varying bitrates and distortion levels, allowing us to evaluate encoder/decoder performance effectively.
**Method of Assessing Moving Images**
The best way to test compressed video is by observing how satisfied viewers are with the final output. Both professionals and casual users can be involved, depending on the test scenario. Since subjective perception varies, multiple assessment methods can be used.
**Contrast Method**
Play live video through the DVR, then compare it directly with other brands’ recordings and display them on a monitor. Carefully observe any distortions, such as blockiness, pixelation, or loss of detail.
**Instrument Method**
In addition to direct observation, use a video synthesis analyzer to examine waveforms and automatically measure technical indicators. High-quality signal cards and video analysis software on a computer can further assist in assessing video quality.
These methods help identify common issues like mosaic effects, jaggies, and dropped frames caused by MPEG compression.
**Automatic Test Method: Image Quality Analyzer**
This method replaces subjective human evaluation with an automated system that provides reliable, fast, and consistent results. The DVR output is fed into the instrument, which evaluates the video using a single numerical score.
The analyzer performs a three-dimensional assessment: spatial, temporal, and full-color analysis of motion sequences. It automatically generates reports including:
- Image quality score
- Frame-by-frame score distribution
- Graphical representation of differences between original and test videos, highlighting any discrepancies.
By combining objective measurements with visual comparisons, this approach ensures a comprehensive evaluation of video quality under various compression schemes.
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