文章作者寫的matlab源代碼，該文章發表在Digital Signal Processing: Ke-Kun Huang , Hui Liu, Chuan-Xian Ren, Yu-Feng Yu and Zhao-Rong Lai. Remote sensing image compression based on binary tree and optimized truncation. Digital Signal Processing, vol. 64, pp. 96-106, 2017. (http://dx.doi.org/10.1016/j.dsp.2017.02.008) 遙感圖像數據非常廣泛，因此需要通過空間設備上的低復雜度算法進行壓縮。具有自適應掃描順序（BTCA）的二叉樹編碼是一個的有效算法。然而，對于大規模遙感圖像，BTCA需要大量的內存，而且不能隨機存取。在本文中，我們提出了一種基于BTCA的新的編碼方法。小波圖像首先劃分為幾個塊，并由BTCA單獨編碼的。根據BTCA的屬性，仔細選擇每個塊的有效截斷點，以優化速率失真的比例，從而獲得更高的壓縮比、更低的內存要求和隨機訪問性能。由于沒有任何熵編碼，所提出的方法簡單快速，非常適合于空間設備。對三個遙感圖像集進行實驗，結果表明它可以顯著提高PSNR、SSIM和VIF，以及主觀視覺體驗。 The remote sensing image data is so vast that it requires compression by low-complexity algorithm on space-borne equipment. Binary tree coding with adaptive scanning order (BTCA) is an effective algorithm for the mission. However, for large-scale remote sensing images, BTCA requires a lot of memory, and does not provide random access property. In this paper, we propose a new coding method based on BTCA and optimize truncation. The wavelet image is first divided into several blocks which are encoded individually by BTCA. According the property of BTCA, we select the valid truncation points for each block carefully to optimize the ratio of rate-distortion, so that a higher compression ratio, lower memory requirement and random access property are attained. Without any entropy coding, the proposed method is simple and fast, which is very suitable for space-borne equipment. Experiments are conducted on three remote sensing image sets, and the results show that it can significantly improve PSNR, SSIM and VIF, as well as subjective visual experience.