The original PNG image is 800x1200 and takes up about 34K. After the images is resized by GraphicsMagick to 320x480 size, the resulting images takes up approximately 37K. (For comparison, if the image is resized with Paint on Windows 7 then the resulting image is 40K.) What gives? The whole point of resizing an image was to save space. How should GraphicsMagick be used to shrink the image size?
PNG is a lossless format and compresses the image data by first performing a step called prediction and then applying the same algorithm used in zlib. The prediction step is a crucial one in order to effectively compress the file, and it is based on the values of earlier neighbors pixels.
So, suppose you have a large PNG in black & white (by that I really mean only black and white, some people confuse that by grayscale sometimes). Also suppose it is not a tiny checkerboard pattern. In many regions of this image, you will have a relatively large white region, and then a relatively large black region, and so on. When the predictor is inside one of these large regions, it has no trouble to correctly predict that the current pixel intensity is exactly equal to the last one. This makes it easier to better compress the data describing your image.
Now, let us downscale this black & white image using some resampling filter different than nearest neighbor (let's say Lanczos). This has a great chance to turn your black & white image into a grayscale one, which has a much greater intensity range. This potentially makes the job of the predictor much harder, and thus the final file size might be larger.
For instance here is a black & white 256x256 PNG image which takes 5440 bytes, a resizing of it (using 3-lobed Lanczos) to 120x120 which now takes 7658 bytes, and another resizing (using nearest neighbor) to 120x120 which occupies 2467 bytes.
PNG is a compressed format. Sometimes trying to compress a maximally compressed item actually results in a larger item. So if the 800x1200 is resized to a smaller size, but the result retains everything that was in the original, because the original is already as minimal as possible, you could see this happen. To demonstrate this, try using 7zip to compress some data with ultra compression. Then try compressing the compressed file. Often the second compressed file will be larger than the first.
Related
I am using a dataset which provides depth images of human, I need to extract the object from this image or at least remove the other distortion in the image that not belong to the human body In Matlab.
a sample of images is shown below:
This is the output when I used
I = imread ('39.jpg');
human = sum(I,3)>10+10;
human
Any way to do that please?
thanks in Advance
For the image you show, where everything is grayscale but something is red, then just do:
so=imread('https://i.stack.imgur.com/hZOQv.jpg');
human=sum(abs(diff(single(so),1,3)),3)>20;
This essentially compares the difference in RGB values of the pixels, and gets the one above a threshold. If you have proper pngs, then the threshold should just be 1, however with jpg artifacts you may need a higher value, for this image 20 does the job.
There are some tiny artefacts in the result image, very likely due to jpg. When you do science, you need to store in png. If you have absolutely no other choice than jpg, then you may have artefacts.
Given a png image and a set of data to write to it, is it possible to overwrite pixels in the existing png in a particular area of interest? For example, If I have a block of data in a rectangle between pixels (0,0) (5,10) would it be possible to write this data as a block into a 10X10 png without any concern for the area not being overwritten? My use case is that I have map tiles where half the data will be in one tile and half in the other, with the blank pixels being white squares. I would like to combine them by simply writing the non-white pixels directly to the existing png in a block without having to open, combine, then re-write the entire png. Does the structure of a png allow this?
I'm loath to claim that this is impossible, but it is certainly complicated.
First of all, pixels of a PNG are (sometimes) interlaced, so you'd have to calculate the locations of your target pixels based on the Adam7 scheme.
Furthermore each row is independently filtered, so you'd have to transform each row of your source using the filter of the target row. Depending on the filter you'd also have to adjust additional bytes on the border of the updated target bytes. Straight from the horse's mouth:
Though the concept is simple, there are quite a few subtleties in the actual mechanics of filtering.
Finally, all the filtered bytes are compressed using a generic compression algorithm called "deflate." Unless you want to decompress the whole thing beforehand, you need to make sure both that (1) your source data can be properly decoded and (2) the bytes near the border of the target bytes are properly compressed in the context of their new neighbors.
I'm not a compression expert, so I won't argue in more detail. One piece of good news is that the algorithm seems to preserve independence between distant regions due to its sliding window scheme: data are only compressed based on data in some preceding range, say 13,000 bytes.
If this seems at all easy to you, give it a try. If you're like me, though, you'll just decode the whole thing, overwrite the pixels as bitmap data, and encode the result.
This is practically impossible because the pixels data (after a row-by-row "filtering") is compressed with ZLIB. And it's practically impossible to change part of a compressed stream.
I have an image with grayscale background and 2 thin lines (1 pixel wide) drawn in color. I'm trying to use various JPEG compression table (luminance and chrominance) to get the best possible result while staying under a certain file size.
The grayscale background compresses well and looks decent. The thin vertical and horizontal color lines get mutilated / smeared. The current JPEG algorithm uses 2x2 sub-sampling on the Cb and Cr channels and the chrominance compression table is fairly aggressive (high compression).
Is there any way to embed a "BMP" type data into JPEG image. Basically specify a color for specific pixels to be applied after the JPEG is de-compressed?
Any other ways clean up how thin color lines get encoded / decoded in JPEG without increasing the overall file size by a lot.
P.S. I'm testing all this stuff in Matlab.
I have 2 images that look nearly identical. The histogram for one (256 bins) has intensities distributed pretty evenly throughout. The other has intensities at the lowest and highest bin. Why would this be? Then wouldnt it appear binary (thats not the case)?
Think about it this way: Imagine you are taking a histogram of two grayscale images with each pixel represented by a color value 0-255. One image contains pixels that all have gray levels of 128. The second image contains a "checkerboard" pattern (pixels alternate between 0 and 255). If you step back far enough that you no longer see individual pixels, they will appear identical to the naked eye. Your brain "averages" the alternating black and white pixels into a field of gray.
This is what your images are doing. The first image has colors distributed evenly throughout the range and the second image has concentrations of specific colors, but if you calculate an average color for the image (and also for sub-sections within the image) you should see similar values for both.
Never trust in your eyes! They will always lie to you.
Consider this silly example that can be illustrative here. An X-Ray 'photo' is nothing more than black and white dots. But as they are small and mixed along the image, your eyes see different shades of gray.
The same can happen in a digital image, where, although the pixels may have the same size, then can be black and white and 'distributed' in the image in such a way that you see it as having more graylevels. This is called halftone.
Without seeing the images it's hard to say, but it sounds like the second may be slightly clipped.
The difference also could just be a slight difference in contrast in the images that's no visible to the naked eye.
I am working with images of size 2 to 4MB. I am working with images of resolution 1200x1600 by performing scaling, translation and rotation operations. I want to add another image on that and save it to photo album. My app is crashing after i successfully edit one image and save to photos. Its happening because of images size i think. I want to maintain the 90% of resolution of the images.
I am releasing some images when i get memory warning. But still it crashes as i am working with 2 images of size 3MB each and context of size 1200x1600 and getting a image from the context at the same time.
Is there any way to compress images and work with it?
I doubt it. Even compressing and decompressing an image without doing anything to it loses information. I suspect that any algorithms to manipulate compressed images would be hopelessly lossy.
Having said that, it may be technically possible. For instance, rotating a Fourier transform also rotates the original image. But practical image compression isn't usually as simple as just computing a Fourier transform.
Alternatively, you could write piecemeal algorithms that chop the image up into bite-sized pieces, transform the pieces and reassemble them afterwards. You might also provide a real-time view of the process by applying the same transform to a smaller version of the full image.
The key will be never to full decode the entire image into memory at full size.
If you need to display the image, there's no reason to do that at full size -- the display on the iPhone is too small to take advantage of that. For image objects that are for display, decode the image in scaled down form.
For processing, you will need to write custom code that works on a stream of pixels rather than an in-memory array. I don't know if this is available on the iPhone already, but you can write it yourself by writing to the libpng library API directly.
For example, your code right now probably looks something like this (pseudo code)
img = ReadImageFromFile("image.png")
img2 = RotateImage(img, 90)
SaveImage(img2, "image2.png")
The key thing to understand, is that in this case, img is not the data in the PNG file (2MB), but the fully uncompressed image (~6mb). RotateImage (or whatever it's called) returns another image of about this same size. If you are scaling up, it's even worse.
You want code that looks more like this (but there might not be any API's for you to do it -- you might have to write it yourself)
imgPixelGetter = PixelDecoderFromFile("image.png")
imgPixelSaver = OpenImageForAppending("image2.png")
w = imgPixelGetter.Width
h = imgPixelGetter.Height
// set up a 90 degree rotate
imgPixelSaver.Width = h
imgPixelSaver.Height = w
// read each vertical scanline of pixels
for (x = 0; x < w; ++x) {
pixelRect = imgPixelGetter.ReadRect(x, 0, 1, h) // x, y, w, h
pixelRect.Rotate(90); // it's now got a width of h and a height of 1
imgPixelSaver.AppendScanLine(pixelRect)
}
In this algorithm, you never had the entire image in memory at once -- you read it out piece by piece and saved it. You can write similar algorithms for scaling and cropping.
The tradeoff is that it will be slower than just decoding it into memory -- it depends on the image format and the code that's doing the ReadRect(). Unfortunately, PNG is not designed for this kind of access to the pixels.