Introduction
With the development of remote sensing technology, high resolution, wide swath and multi-waveband make data from sensor increase greatly. Image compression should be finished onboard to lighten the burden on the communication system. Therefore remote sensing image compression becomes one of the most pressing tasks. There are many kinds of image compression approaches based on different theories. To evaluate whether an approach is good or not, people’s attention is mainly focused on bit ratio, degree of difficulty in performing the approach and quality of the reconstructed image. Among the three, image quality measure is the most fundamental but the most unoperatable because by far there is no criterion of image quality evaluation which can be accepted generally all. Significant compression is achievable only by lossy algorithms. To find the answer to what degree the reconstructed image can be accepted by the users, we make an approach to the measurement of high-resolution remote sensing image quality and get some initial results.

General methods for image quality evaluation
Generally speaking, image quality evaluation has something to do with the purpose of the image. Image quality has two implications: fidelity and intelligibility. The former describes how the reconstructed image differs from the original one, with mean-square-error as a typical example, and the latter shows the ability through which the image can offer information to people, with classification-accuracy as a typical example. Both are foundational in measuring the image quality.

It must be pointed out that fidelity is not always objective and intelligibility is not always subjective. Whether an objective measure on image quality is efficient or not depends strongly on its accordance with subjective measure. But this consistence is difficult to find out. The reason why the result of objective measure is inconsistent with that of subjective by the user in many cases is that firstly man’s knowledge on visual characteristics is not enough to establish an accurate visual model and secondly people has no better methods to describe objective measure. For example, interpreters usually tend to pay their attention to that part of an image where distortion is the most or they are most interested in, such as edges or texture, but objective measure cannot describe it accurately.

Research on consistence between subjective and objective measures
Methods for image quality evaluation can be classified as objective and subjective measures. By objective measures some parameters are calculated to indicate the reconstructed image quality and by subjective measure viewers read images directly to determine their quality. The ultimate goal of research on image quality evaluation is to develop a quantitative measure that will consistently be used as a substitute. Then we conduct a study including several aspects as follows.

Establishment of a standard-image base
Until now, when those who study image compression evaluate their methods they usually choose some typical images such as portrait of Lena or Girl and calculate the peak signal-to-noise-ratio to indicate the quality of reconstructed image. Since this kind of test-image is different from that of remote sensing, the conclusion isn’t suitable for all kinds of images. So the standard-image base to meet special needs is established and the compression method to be chosen would be suitable for most of the images in the database. On the other hand, the ultimate goal of image quality evaluation is to find out the consistence between subjective and objective measure, which can be obtained only by large quantities of tests. Therefore the standard-images for test should be more comprehensive.

What kind of image should be chosen as standard-image? We consider it in two ways. These images must be firstly of different types and several resolution levels, which can offer various information, and secondly of various statistical characters such as histogram and entropy in order that the standard-images are more comprehensive in statistical characters.

Objective measure
Parameters of objective measure are considered in two aspects. One is the difference between the reconstructed image and the original such as Mean-Square-Error (MSE) and Signal-to-Noise-Ratio (SNR); another is the approximation between them such as fidelity (BZD) and resemblity (XSD). The calculation formulas are as follows:

Mean-Square-Error



Signal-to-Noise-Ratio



Fidelity



Resemblity



There is another parameter that combines both subjective and objective measures named Perceptional Error (PE).