TIFF图生成和压缩：R与GIMP对比IrfanView与Photoshop文件大小 [英] TIFF plot generation and compression: R vs. GIMP vs. IrfanView vs. Photoshop file sizes
问题描述
我生成了一些高分辨率的出版质量图，例如
I generated some high resolution publication quality plots for example
library(plot3D)
Volcano<-volcano
zf=10 #zoom factor
tiff("Volcano.tif", width=1800*zf, height=900*zf, res=175*zf, compression="lzw")
image2D(z = Volcano, clab = "height, m",colkey = list(dist = -0.20, shift = 0.15,side = 3, length = 0.5, width = 0.5,cex.clab = 1.2, col.clab = "white", line.clab = 2,col.axis = "white", col.ticks = "white", cex.axis = 0.8))
dev.off()
文件为22 MB。
现在我用GIMP和打开文件而不做任何其他事情我将其导出为Volcano gimp.tif（不要更改分辨率） ，或做任何其他事情）。 GIMP生成一个1.9 MB的文件（Volcano gimp.tif）。
Now I open the file with GIMP and without doing anything else I export it as "Volcano gimp.tif" (don't change resolution, or do anything else). GIMP generates a file ("Volcano gimp.tif") that is 1.9 MB.
imagemagick 报告类似图像统计：
$ identify Volcano.tif
Volcano.tif TIFF 18000x9000 18000x9000+0+0 8-bit DirectClass 22.37MB 0.000u 0:00.000
$ identify "Volcano gimp.tif"
Volcano gimp.tif TIFF 18000x9000 18000x9000+0+0 8-bit DirectClass 1.89MB 0.000u 0:00.000
即使使用识别-verbose 这两个文件似乎相似。
even using identify -verbose the 2 files appear to be similar.
这些文件有什么区别？为什么他们的文件大小如此不同？
What is the difference between these files? Why do they have so different file sizes?
更新：好的，事情变得越来越疯狂了。我用IrfanView做了同样的事情，我得到了不同的文件大小。初始文件是从 R 生成的 Volcano.tif ，其中 compression =lzw。检查 Volcano irfan.tif 和 Volcano gimp.tif 的大小不同但所有其他统计数据都相同。内存占用，DPI，颜色，分辨率相同。磁盘大小不同。
UPDATE: OK, things are getting crazier. I did the same thing with IrfanView and I get different file sizes. The initial file is the Volcano.tif generated from R with compression="lzw". Check how Volcano irfan.tif and Volcano gimp.tif differ in size but all other stats are the same. Memory footprint, DPI, Colors, Resolution is identical. Disk size is different.
更新2： Adobe Photoshop将文件保存至2.6 MB
UPDATE 2: Adobe Photoshop saves the file down to 2.6 MB
WinRar报告原始R生成的TIFF是高度可压缩的（从22MB - > 3.6MB）
WinRar reports that the original R generated TIFF is highly compressible (from 22MB ->3.6MB)
更新3：此问题可能类似于蒙太奇/加入2张幻灯片中的2张TIFF图像1行瓷砖不会丢失质量
更新4：可以在此处找到R生成的TIFF文件 http://ge.tt/7ZvRd4C1/v/0?c
UPDATE 4: The R generated TIFF file can be found here http://ge.tt/7ZvRd4C1/v/0?c
推荐答案
显然R使用的TIFF LZW压缩器没有使用impo rtant选项（TIFF预测器），它导致一个非常大的文件。当数据压缩能够识别数据中的对称性/冗余时，数据压缩效果最佳。在这种情况下，图像数据由包含红色，绿色和蓝色8位值的24位（3字节）像素组成。标准LZW压缩查看重复模式的字节流。如果它将彩色图像简单地看作字节流，它将看到重复的3字节模式，而不是重复恒定颜色的模式。对数据启用TIFF预测器会导致差分滤波器存储每个像素与其邻居的增量。如果相邻像素是相同的颜色，它将存储0。一个长0的字符串压缩比重复至少3个字节长的非零的模式要好得多。
Apparently the TIFF LZW compressor used by R is not making use of an important option (the TIFF predictor) which is leading to an extremely large file. Data compression works best when it can recognize symmetries/redundancies in the data. In this case, the image data is composed of 24-bit (3-byte) pixels containing red, green and blue 8-bit values. Standard LZW compression looks at a stream of bytes for repeating patterns. If it looks at the color image simply as a stream of bytes, it will see repeating patterns of 3-bytes instead of repeating patterns of constant color. Enabling the TIFF predictor on the data causes a differencing filter to store the delta of each pixel with its neighbor. If the neighboring pixels are the same color, it will store 0's. A long string of 0's compresses much better than repeating patterns of non-zeros which are at least 3 bytes long.
这是一个如何在6像素上工作的示例线。编码时，预测器从右边缘开始，并在每条扫描线上向左移动：
Here is an example of how it works on a 6 pixel line. When encoding, the predictor starts from the right edge and works left for each scan line:
Original data:
2A 50 40 2A 50 40 2A 50 40 2A 50 40 2A 50 40 2A 50 40 (6 pixels of the same color)
After horizontal differencing (TIFF predictor):
2A 50 40 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
The data is much more compressible after the predictor since long runs of the same value (0x00) are easier for LZW to compress.
结论：这应该作为R压缩代码的所有者提交的错误，因为使用LZW没有预测器的全彩色图像会产生不良结果。与此同时，需要一种解决方法来更有效地压缩它。
Conclusion: This should be filed as a bug against the owner of the R compression code since using LZW on full color images without the predictor produces poor results. In the mean time, a workaround is needed to compress it more efficiently.
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