基于KLT和HEVC的嵌入式高光谱图像实时压缩

现有追求高压缩质量的高光谱图像压缩算法普遍存在计算复杂度高、离线式处理、嵌入式平台实现难度大等问题，目前很难得到实际应用。为解决以上问题，设计一种基于KLT和HEVC的嵌入式高光谱图像实时压缩方法。首先基于KLT去除谱间相关性，然后基于HEVC去除空间相关性并完成量化编码的过程。基于NVIDIA Jetson TX1平台，设计并实现了CPU和GPU异构并行压缩处理系统。利用真实数据集对所设计算法和所实现平台进行了性能及可行性验证。实验结果表明:在相同压缩比下，与离散小波变换（DWT）+JPEG2000算法相比，该系统明显提升了重建精度，在峰值信噪比（PSNR）方面平均提高了1.36 dB；同时，相比CPU，在GPU中进行KLT计算也至多可缩短33%的运行时间。

Embedded real-time compression for hyper-spectral images based on KLT and HEVC

The existing hyperspectral image compression algorithms that aim at high compression quality generally have problems such as high computational complexity, off-line processing, and difficulty in implementing an embedded platform. They are difficult to be implemented in practical applications at present. To resolve the above problems, a real-time compression method for embedded hyperspectral images based on Karhunen-Loeve Transform (KLT) and HEVC (High Efficiency Video Coding) was designed. Firstly, the inter-spectral correlation was reduced by KLT. Then, the spatial correlation was removed by HEVC. Finally, the process of quantization and entropy coding was accomplished by HEVC. Based on NVIDIA Jetson TX1 platform, a heterogeneous parallel compression system which utilizes both the CPU and GPU was designed and implemented. Using real data sets, the performance of the designed algorithm and the practicability of the implemented platform were verified. The experimental results show that compared with the Discrete Wavelet Transform (DWT)+JPEG2000 algorithm, the reconstruction accuracy is improved significantly under the same compression ratio. The Peak Signal-to-Noise Ratio (PSNR) is increased by 1.36 dB on average; at the same time, compared with CPU, performing KLT calculations on GPU can also reduce the runtime by 33% at most.