I'm writing a tool to detect images on our website that should be flagged for manual intervention to reduce file size. If a "large" image is 100K that might be fine, but if a "small" image is 100K, someone forgot to flatten it or compress it.
I'm looking at the "file density" of an image as the ratio filesize/(height x width). Is there a term for this? Is there some guidance about what a reasonable range for this density should be, so that I can flag images? Or am I thinking about this wrong?
Yes, if the file size is given in bits, then that fraction is known as the bitrate in bits per pixel (bpp) - as sascha points out. For example, an uncompressed image is usually 24 bit (8bit/channel * 3 channels (r,g,b)). Anything at this or higher bitrates is (most often) not compressed.
In general, lossless compression can be achieved at bitrates of about 12bpp (a 2:1 compression ratio). Normally you can aim at much lower bitrates (e.g., 1 bit per pixel, 24:1 compression ratio) and expect decent quality, but it'll depend on the images you're dealing with.
Related
We're building an online video editing service. One of the features allows users to export a short segment from their video as an animated gif. Imgur has a file size limit of 2Mb per uploaded animated gif.
Gif file size depends on number of frames, color depth and the image contents itself: a solid flat color result in a very lightweight gif, while some random colors tv-noise animation would be quite heavy.
First I export each video frame as a PNG of the final GIF frame size (fixed, 384x216).
Then, to maximize gif quality I undertake several gif render attempts with slightly different parameters - varying number of frames and number of colors in the gif palette. The render that has the best quality while staying under the file size limit gets uploaded to Imgur.
Each render takes time and CPU resources — this I am looking to optimize.
Question: what could be a smart way to estimate the best render settings depending on the actual images, to fit as close as possible to the filesize limit, and at least minimize the number of render attempts to 2–3?
The GIF image format uses LZW compression. Infamous for the owner of the algorithm patent, Unisys, aggressively pursuing royalty payments just as the image format got popular. Turned out well, we got PNG to thank for that.
The amount by which LZW can compress the image is extremely non-deterministic and greatly depends on the image content. You, at best, can provide the user with a heuristic that estimates the final image file size. Displaying, say, a success prediction with a colored bar. You'd can color it pretty quickly by converting just the first frame. That won't take long on 384x216 image, that runs in human time, a fraction of a second.
And then extrapolate the effective compression rate of that first image to the subsequent frames. Which ought to encode only small differences from the original frame so ought to have comparable compression rates.
You can't truly know whether it exceeds the site's size limit until you've encoded the entire sequence. So be sure to emphasize in your UI design that your prediction is just an estimate so your user isn't going to disappointed too much. And of course provide him with the tools to get the size lowered, something like a nearest-neighbor interpolation that makes the pixels in the image bigger. Focusing on making the later frames smaller can pay off handsomely as well, GIF encoders don't normally do this well by themselves. YMMV.
There's no simple answer to this. Single-frame GIF size mainly depends on image entropy after quantization, and you could try using stddev as an estimator using e.g. ImageMagick:
identify -format "%[fx:standard_deviation]" imagename.png
You can very probably get better results by running a smoothing kernel on the image in order to eliminate some high-frequency noise that's unlikely to be informational, and very likely to mess up compression performance. This goes much better with JPEG than with GIF, anyway.
Then, in general, you want to run a great many samples in order to come up with something of the kind (let's say you have a single compression parameter Q)
STDDEV SIZE W/Q=1 SIZE W/Q=2 SIZE W/Q=3 ...
value1 v1,1 v1,2 v1,3
After running several dozens of tests (but you need do this only once, not "at runtime"), you will get both an estimate of, say, , and a measurement of its error. You'll then see that an image with stddev 0.45 that compresses to 108 Kb when Q=1 will compress to 91 Kb plus or minus 5 when Q=2, and 88 Kb plus or minus 3 when Q=3, and so on.
At that point you get an unknown image, get its stddev and compression #Q=1, and you can interpolate the probable size when Q equals, say, 4, without actually running the encoding.
While your service is active, you can store statistical data (i.e., after you really do the encoding, you store the actual results) to further improve estimation; after all you'd only store some numbers, not any potentially sensitive or personal information that might be in the video. And acquiring and storing those numbers would come nearly for free.
Backgrounds
It might be worthwhile to recognize images with a fixed background; in that case you can run some adaptations to make all the frames identical in some areas, and have the GIF animation algorithm not store that information. This, when and if you get such a video (e.g. a talking head), could lead to huge savings (but would throw completely off the parameter estimation thing, unless you could estimate also the actual extent of the background area. In that case, let this area be B, let the frame area be A, the compressed "image" size for five frames would be A+(A-B)*(5-1) instead of A*5, and you could apply this correction factor to the estimate).
Compression optimization
Then there are optimization techniques that slightly modify the image and adapt it for a better compression, but we'd stray from the topic at hand. I had several algorithms that worked very well with paletted PNG, which is similar to GIF in many regards, but I'd need to check out whether and which of them may be freely used.
Some thoughts: LZW algorithm goes on in lines. So whenever a sequence of N pixels is "less than X%" different (perceptually or arithmetically) from an already encountered sequence, rewrite the sequence:
018298765676523456789876543456787654
987678656755234292837683929836567273
here the 656765234 sequence in the first row is almost matched by the 656755234 sequence in the second row. By changing the mismatched 5 to 6, the LZW algorithm is likely to pick up the whole sequence and store it with one symbol instead of three (6567,5,5234) or more.
Also, LZW works with bits, not bytes. This means, very roughly speaking, that the more the 0's and 1's are balanced, the worse the compression will be. The more unpredictable their sequence, the worse the results.
So if we can find out a way of making the distribution more **a**symmetrical, we win.
And we can do it, and we can do it losslessly (the same works with PNG). We choose the most common colour in the image, once we have quantized it. Let that color be color index 0. That's 00000000, eight fat zeroes. Now we choose the most common colour that follows that one, or the second most common colour; and we give it index 1, that is, 00000001. Another seven zeroes and a single one. The next colours will be indexed 2, 4, 8, 16, 32, 64 and 128; each of these has only a single bit 1, all others are zeroes.
Since colors will be very likely distributed following a power law, it's reasonable to assume that around 20% of the pixels will be painted with the first nine most common colours; and that 20% of the data stream can be made to be at least 87.5% zeroes. Most of them will be consecutive zeroes, which is something that LZW will appreciate no end.
Best of all, this intervention is completely lossless; the reindexed pixels will still be the same colour, it's only the palette that will be shifted accordingly. I developed such a codec for PNG some years ago, and in my use case scenario (PNG street maps) it yielded very good results, ~20% gain in compression. With more varied palettes and with LZW algorithm the results will be probably not so good, but the processing is fast and not too difficult to implement.
I'm writing an application that needs to compress and scale an image before sending it as an mms which has a maximum file size of 600kb. I'm looking for an algorithm that will help estimate the maximum surface area of a jpeg with a file size < 600kb that accounts for a reduction in quality by some percent between. Thanks.
The compression ratio gotten for a specific quality level is pretty consistent. For example, you may find that at a quality level of 80 with color subsampling, you get 14:1 compression. You can do some tests with your JPEG encoder at various quality levels to see the compression ratio achieved and then work backwards to find the largest image size you can compress for a given quality level.
I have an optimally compressed png that I'm rotating by 1 degree using ImageMagick -
convert -rotate 1 crab.png crab-rotated.png
The size goes from 74 KB to 167 KB. How do I minimize that increase?
Orginal:
Rotated:
The increase in file size is probably due to less efficient compression. You won't be able to do anything about that unless you decrease the compression level (-quality option) or use a more efficient but lossy compression method (e.g. JPEG).
Here's the reason why I think this happens (I hope somebody can correct me if I am wrong). By rotating the image, you are introducing spatial frequencies that were not present in the original image. If these frequencies are not suppressed during compression, than the file size will inevitably increase. However, suppressing these frequencies may degrade the quality of your image. It's a delicate balance.
The amount of increase (or decrease) in filesize after rotation depends on the frequencies already present in the image, i.e. it is image-specific.
For example, I have an 1024*768 JPEG image. I want to estimate the size of the image which will be scaled down to 800*600 or 640*480. Is there any algorithm to calculate the size without generating the scaled image?
I took a look in the resize dialog in Photoshop. The size they show is basically (width pixels * height pixels * bit/pixel) which shows a huge gap between the actual file size.
I have mobile image browser application which allow user to send image through email with options to scale down the image. We provide check boxes for the user to choose down-scale resolution with the estimate size. For large image (> 10MB), we have 3 down scale size to choose from. If we generate a cached image for each option, it may hurt the memory. We are trying to find the best solution which avoid memory consumption.
I have successfully estimated the scaled size based on the DQT - the quality factor.
I conducted some experiments and find out if we use the same quality factor as in the original JPEG image, the scaled image will have size roughly equal to (scale factor * scale factor) proportion of the original image size. The quality factor can be estimate based on the DQT defined in the every JPEG image. Algorithm has be defined to estimate the quality factor based on the standard quantization table shown in Annex K in JPEG spec.
Although other factors like color subsampling, different compression algorithm and the image itself will contribute to error, the estimation is pretty accurate.
P.S. By examining JPEGSnoop and it source code, it helps me a lot :-)
Cheers!
Like everyone else said, the best algorithm to determine what sort of JPEG compression you'll get is the JPEG compression algorithm.
However, you could also calculate the Shannon entropy of your image, in order to try and understand how much information is actually present. This might give you some clues as to the theoretical limits of your compression, but is probably not the best solution for your problem.
This concept will help you measure the differences in information between an all white image and that of a crowd, which is related to it's compressibility.
-Brian J. Stinar-
Why estimate what you can measure?
In essence, it's impossible to provide any meaningful estimate due to the fact that different types of images (in terms of their content) will compress very differently using the JPEG algorithm. (A 1024x768 pure white image will be vastly smaller than a photograph of a crowd scene for example.)
As such, if you're after an accurate figure it would make sense to simply carry out the re-size.
Alternatively, you could just provide an range such as "40KB to 90KB", based on an "average" set of images.
I think what you want is something weird and difficult to do. Based on JPG compression level some images are heavier that others in terms of heavier (size).
My hunch for JPEG images: Given two images at same resolution, compressed at the same quality ratio - the image taking smaller memory will compress more (in general) when its resolution is reduced.
Why? From experience: many times when working with a set of images, I have seen that if a thumbnail is occupying significantly more memory than most others, reducing its resolution has almost no change in the size (memory). On other hand, reducing resolution of one of the average size thumbnails reduces the size significantly. (all parameters like original/final resolution and JPEG quality being the same in the two cases).
Roughly speaking - higher the entropy, less will be the impact on size of image by changing resolution (at the same JPEG quality).
If you can verify this with experiments, maybe you can use this as a quick method to estimate the size. If my language is confusing, I can explain with some mathematical notation/psuedo formula.
An 800*600 image file should be roughly (800*600)/(1024*768) times as large as the 1024*768 image file it was scaled down from. But this is really a rough estimate, because the compressibility of original and scaled versions of the image might be different.
Before I attempt to answer your question, I'd like to join the ranks of people that think it's simpler to measure rather than estimate. But it's still an interesting question, so here's my answer:
Look at the block DCT coefficients of the input JPEG image. Perhaps you can find some sort of relationship between the number of higher frequency components and the file size after shrinking the image.
My hunch: all other things (e.g. quantization tables) being equal, the more higher frequency components you have in your original image, the bigger the difference in file size between the original and shrinked image will be.
I think that by shrinking the image, you will reduce some of the higher frequency components during interpolation, increasing the possibility that they will be quantized to zero during the lossy quantization step.
If you go down this path, you're in luck: I've been playing with JPEG block DCT coefficients and put some code up to extract them.
The JPEG compression encoding process splits a given image into blocks of 8x8 pixels, working with these blocks in future lossy and lossless compressions. [source]
It is also mentioned that if the image is a multiple 1MCU block (defined as a Minimum Coded Unit, 'usually 16 pixels in both directions') that lossless alterations to a JPEG can be performed. [source]
I am working with product images and would like to know both if, and how much benefit can be derived from using multiples of 16 in my final image size (say, using an image with size 480px by 360px) vs. a non-multiple of 16 (such as 484x362). In this example I am not interested in further alterations, editing, or recompression of the final image.
To try to get closer to a specific answer where I know there must be largely generalities: Given a 480x360 image that is 64k and saved at maximum quality in Photoshop [example]:
Can I expect any quality loss from an image that is 484x362
What amount of file size addition can I expect (for this example, the additional space would be white pixels)
Are there any other disadvantages to growing larger than the 8px grid?
I know it's arbitrary to use that specific example, but it would still be helpful (for me and potentially any others pondering an image size) to understand what level of compromise I'd be dealing with in breaking the non-8px grid.
The key issue here is a debate I've had is whether 8-pixel divisible images are higher quality than images that are not divisible by 8-pixels.
8 pixels is the cutoff. The reason is because JPEG images are simply an array of 8x8 DCT blocks; if the image resolution isn't mod8 in both directions, the encoder has to pad the sides up to the next mod8 resolution. This in practice is not very expensive bit-wise; what's much worse are the cases when an image has sharp black lines (such as a letterboxed image) that don't lie on block boundaries. This is especially problematic in video encoding. The reason for this being a problem is that the frequency transform of a sharp line is a Gaussian distribution of coefficients--resulting in an enormous number of bits to code.
For those curious, the most common method of padding edges in intra compression (such as JPEG images) is to mirror the lines of pixels before the edge. For example, if you need to pad three lines and line X is the edge, line X+1 is equal to line X, line X+2 is equal to line X-1, and line X+3 is equal to line X-2. This quite effectively minimizes the cost in transform coefficients of the extra lines.
In inter coding, however, the padding algorithms generally simply duplicate the last line, because the mirror method does not work well for inter compression, such as in video compression.
Sometimes you need to use 16 pixel boundaries rather than 8 because of subsampling; every 2nd pixel is thrown away during the encoding process, and those 8x8 DCT blocks started out as 16x16 and will decode back to 16x16. This won't be a problem at the highest quality settings.
A JPG with sizes being multiplies of 8 can also be rotated/flipped with no quality loss. For example gthumb can do this on Linux.
The image dimensions being multiples of 8 or 16 is not going to affect the size on disk very much, but you can get dramatic savings if you can line up the visual contents to the 8x8 pixel grid, such as if there is a repeating pattern or texture in the image.
What Tometzky said. If you don't have the correct multiple, the lossless flip and rotate algorithms don't work. That's because the padding on the right/bottom that can be safely ignored now ends up on the left/top, where it can't.