众所周知，数据量的庞大,致使高光谱图像数据的应用受到很大限制。这种庞大的数据量对于许多情况，尤其对于卫星数据链路，由于受带宽和星上存储能力的局限，致使不能实时进行数据传输，因此必须使用有损压缩方式来减小高光谱图像的数据量。但由于有损压缩带来的信息丢失，对高光谱数据的不同后续应用影响不同，因此压缩图像的质量评价技术得到了广泛重视。为使人们对这一质量评价技术有一定了解，首先对高光谱图像压缩方法进行简单介绍；然后对现存的客观失真参数评价、应用算法统计结果评价、相似敏感度标准抽取评价等主要的压缩质量评价技术进行综述，同时比较它们的优缺点，并在此基础上，提出了一种基于最优性能的质量评估框架；最后对该技术今后的研究发展进行了展望。

Applications for hyperspectral image data are still in their infancy as handling the significant size of the data presents a challenge for the user community. In some cases, especially in satellite data link, limited by the available bandwidth and the onboard storage capacity, lossy compression techniques have to be used. Hyperspectral imagery applications appear different sensitivities to various of degradations caused by different lossy compression methods. The evaluation of quality of compressed hyperspectral images comes in growing interest over the past few years.In the paper we first reviewed some major hyperspectral image compression methods. Second we summarized the existing evaluation approaches including objective distortion criteria, statistical applications algorithms results evaluation and extraction of distortion criteria with same sensitivity. The merits and drawbacks of the approaches were discussed as well.Then a new evaluation scheme based on optimal performance is proposed. In the end, the future trend of the evaluation technique was pointed out, in personal opinion.