GB/T 38199-2019 in English
VALIDEvaluation methods for optical image compression quality of land observation satellites
- Issued on:2019-10-18
- Implemented on:2020-05-01
- File Format:PDF
- Delivery:Within 1 day
$125.00
《GB/T 38199-2019陆地观测卫星光学影像压缩质量评价方法》由TC425(全国宇航技术及其应用标准化技术委员会)归口,主管部门为国家标准化管理委员会。
Introduction
1. Background and significance of standard formulation
With the increase in the number of land observation satellites launched in my country and the in-depth development of remote sensing technology in scientific research and application fields, the requirements for image quality are constantly increasing. In order to standardize the evaluation of optical image compression effects and ensure the effectiveness of compression technology in data storage and transmission, the GB/T 38199-2019 standard came into being.
2. Comparison of standard frameworks
| Standard dimensions | Simulated data source requirements | Subjective evaluation | Objective evaluation | Evaluation result judgment |
|---|---|---|---|---|
| Simulated data source requirements | The spatial resolution and band settings are consistent with those of actual satellites; the images cover a variety of terrains and phases. | - Soft copy display: monitor calibration, support 100%~400% magnification ratio- Hard copy output: print media selection and resolution setting | Block standard deviation ratio (≥80%), grayscale difference mean (<0.4), peak signal-to-noise ratio (≥42dB or 45dB) | Comprehensive score ≥7 points and subjective score ≥3 points are acceptable |
3. Implementation suggestions and application scenarios
Hardware equipment selection suggestions:Use high-precision scanners and image processing equipment with matching quantization bits to ensure the authenticity of the simulated data source.
Evaluation process optimization suggestions:In the subjective evaluation stage, it is recommended to use a team of more than 10 experts and combine statistical methods to improve the reliability of the results.
Future technology evolution direction: Explore automatic evaluation algorithms based on deep learning to improve the efficiency and accuracy of image quality assessment.

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