In the era of Qin Shi Huang, unifying language, weights, and measures significantly boosted productivity. Today, hard disk recorders (DVRs) are in a chaotic state, with various compression techniques constantly emerging, making it difficult to compare video quality fairly. The absence of a unified video test signal source means that different DVRs are not competing on the same starting line. The video signal captured by the camera is recorded by the DVR and then displayed on the monitor. To ensure fair comparison, all DVRs should use the same standard video test signal. This approach simplifies the evaluation process.
The test system framework involves a computer monitor, video analysis software, a VGA monitor for comparing static standard video signals, multiple DVRs, video signals to be measured, and visual comparison tools. A video composite analyzer, monitor A, and monitor B are used alongside standard video signals, audio signals, motion images, a DVD video standard test pattern generator, cameras, and computer monitors. The system also includes moving images that resemble standard video signals for comparison.
Evaluating moving image compression efficiency is more complex than testing static images. Factors such as image quality, data rate, image complexity, and coding algorithms affect the performance of compressed video systems. Static test signals cannot fully reflect real-world conditions. Instead, actual scenes with dynamic changes should be used to better assess the system's performance. When moving images are digitized, recorded, and transmitted through different compression methods, they can suffer from distortion or damage. Therefore, selecting an appropriate moving image is crucial. The chosen video should avoid story-driven content to prevent distractions and instead focus on varying levels of detail, brightness, contrast, and color saturation.
The selected moving image should include elements such as different proportions of moving subjects against the background, varied movement directions (straight, curved, or irregular), basic colors like red, green, blue, yellow, black, white, and more, different speeds of movement, camera movements (tracking, fixed, panning, zooming), color variations, and backgrounds that are either simple or complex. These features cover the fundamental aspects of moving images.
Based on these characteristics, video coding systems typically consist of two main components: traditional texture and motion information coding (motion compensation), and specific shape coding in standards like MPEG-4. Different compression methods and ratios produce varying bitrates and levels of distortion, which directly impact the encoder/decoder’s performance.
To evaluate moving images, the best method is to assess how satisfied observers are with the video processed by the DVR. Both professionals and non-professionals can be involved. Subjective evaluations can vary, so several assessment methods can be used depending on the situation.
The **contrast method** involves playing live video through one DVR, then through others, and comparing the results on a direct monitor. Observers carefully examine the moving image for any damage or degradation.
The **instrument method** uses a video synthesis analyzer connected to the monitor to analyze waveforms and automatically test technical indicators. High-quality signal conversion cards and video analysis software on a computer can also be used for objective measurements.
These methods help identify common issues caused by MPEG compression, such as blocky artifacts, jagged edges, mosquito noise, and frame drops, making them useful for detailed analysis.
The **automatic test method** uses an image quality analyzer to replace subjective human evaluation. It provides fast, accurate, and consistent results by measuring image quality numerically. The analyzer performs a three-dimensional evaluation of complex motion sequences—spatial, temporal, and full-color analysis.
The image quality analyzer automatically generates the following results:
- Image quality score
- Frame-by-frame score distribution
- Graphical representation showing differences between the original and test video, highlighting any discrepancies.
This comprehensive approach ensures a reliable and objective assessment of video quality across different compression methods.
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