I'm working with lots of camera's which capture in BG bayer pattern natively.
Now, every time I record some data, I save it to the disk in the raw bayer pattern, in an avi container. The problem is, that this really adds up after a while. After one year of research, I have close to 4TB of data...
So I'm looking for a lossless codec to compress this data. I know I could use libx264 (with --qp 0), or huffYUV, dirac or jpeg2000, but they all assume you have RGB or YUV data. It's easy enough to convert the bayered data to RGB, and then compress it, but it kind of defeats the purpose of compression if you first triple the data. This would also mean that the demoasicing artefacts introduced by debayering would also be in my source data, which is also not too great. It would be nice to have a codec that can work on the bayered data directly.
Even more nice would be that the solution would involve a codec that is already supported by gstreamer (or ffmpeg), since that's what I am already using.
A rather late suggestion, maybe useful for others..
It helps to deinterleave the Bayer pattern into four quadrants and then treat that image as grayscale. The sub-images (e.g. all red pixels in top left) have half the spatial resolution, but their pixels are more highly correlated. This leads to lower residuals from predictors using nearby pixels and therefore to better compression ratios.
I've seen this reach 2-3x lossless compression on 12-bit raw camera data.
If a commercial solution is ok, check out Cineform. I've used their sdk for a custom video compressor and it works great plus they have some great tools for processing the raw video.
Or if you prefer the open source route check out Elphel JP4.
All I know about Bayer Patterns I learned from Wikipedia, but isn't conversion to RGB more of a deinterlacing than a tripling? Doesn't the resolution for red and blue go down by a factor of 4 and green by a factor of 2? If so, a lossless image compression scheme like lossless jpeg might be just the thing.
Related
For example in audio codecs like Opus, MDCT is used with 50% percent overlap to avoid ringing artifacts. Why a similar approach is not used in image codecs. e.g., JPEG uses non-overlapping 8x8 blocks ?
Later lossy image codecs like JPEG2000 do use overlapped transforms, but these techniques just weren't around when JPEG was being defined. The wavelet transform that JPEG2000 is based on hadn't been invented yet, and time-domain anti-aliasing techniques like MDCT were extremely new.
For the MDCT in particular, as far as I know it is not used for image compression at all, even Today. I would guess that's because its basis vectors are asymmetric, which makes it intuitively difficult to choose for imaging applications.
I'm wonder if there is a way to automatically choose a reasonable JPEG compression level in OpenCV?
The current JPEG sizes I'm getting are too large, and nailing it to a fixed value feels dirty. If I recall such features existed in image editors such as Dreamweaver. If there is no such features, i'm also wondering if somebody knows of an algorithm that is able to estimate this parameter without performing hard disk IO.
std::vector<int> params;
params.push_back(CV_IMWRITE_JPEG_QUALITY);
params.push_back(magic); //Want a way to estimate magic
cv::imwrite("my.jpg",image,params);
Unfortunately, to "optimize" JPEG compression, one would have to learn and apply many technical details about the JPEG compression. Because of this, many libraries do not offer the full suite of adjustment parameters. The 0-100 JPEG quality parameter is already a good compromise.
ImageMagick may have such functionality.
You are looking for a way to "automatically choose a reasonable JPEG compression level in OpenCV".
However, "reasonable" is subjective, and depends on the the image owner's perception of what features are important in the given image. This means the perception can be different for every combination of (different owners) x (different images).
The short answer
No, OpenCV does not currently offer this functionality.
The "sysadmin" answer
Look at OpenCV ImageMagick integration.
http://www.imagemagick.org/discourse-server/viewtopic.php?f=22&t=20333&start=45
The quick and dirty answer
Use method of bisection (0, 100, 50, 75, 87, ...) to search for a JPEG quality level that will approach a specified output file size.
Secant method may also be applicable.
Edited: Newton's method is probably not useful, because one cannot obtain the first derivative of the quality-file size curve without an analytical model.
Obviously this is too inefficient for practical every-day use, so it is not provided by the library.
If you want to use it, you have to implement it yourself with your own choice of techniques.
To avoid disk I/O, use cv::imencode which writes to memory instead of to disk.
The slightly longer answer
Although it doesn't implement this functionality, it is obvious that it is a nice feature to have.
If someone is willing to implement it with code quality good for use in OpenCV, OpenCV may consider accept it.
The yet longer answer
OpenCV uses jpeglib, or optionally libjpeg-turbo, and both libraries allow one to configure the technical details of JPEG compression.
Below I will focus on these technical details.
Read first: JPEG compression on Wikipedia
Of the JPEG compression pipeline, three of the compression steps can be configured by users of jpeglib or libjpeg-turbo:
Chroma subsampling
After the conversion from RGB to YCbCr, the chroma (color-carrying) channels: Chroma-blue and Chroma-red, are optionally stored in a lower resolution relative to the Luminance (Y) channel, also known as the Intensity or Grayscale channel, the latter is always stored at full resolution.
Most JPEG decoders can support these downsampling factors:
(1, 1) - no subsampling
(1, 2), (2, 1), (2, 2) - moderate subsampling, where one or both dimensions may be subsampled by 2.
(1, 4), (2, 4), (4, 2), (4, 1) - heavy subsampling. Note that the original JPEG specification forbids some of these combinations, but most JPEG decoders are able to decode them nevertheless.
Quantization table
Each JPEG image can define a quantization table for the "AC coefficients" of the DCT transformed coefficients
Each JPEG image can define a quantization table for the "DC coefficient" (i.e. the average value of the 8x8 block) computed from the DCT transform.
Quantization is the "lossy step" of JPEG compression. So, a technical user will have to decide how much loss (quantization) is acceptable, and then configure the quantization table accordingly.
Huffman table
Huffman coding is a lossless compression technique. In other words, if one could really spend time optimizing the Huffman coding table based on the statistics of the quantized DCT coefficients of the whole image, one can often construct a good Huffman table to optimize compression without having to trade off quality.
Unfortunately, the reality is more complicated, and such optimization is often not enabled.
It requires keeping all DCT coefficients in memory, for the whole image. This bloats memory usage.
Writing to the file cannot start until everything is in memory. In contrast, if a library chooses the quantization table and Huffman table up-front, without looking at the statistics of the DCT coefficients, then the library would be able to write to the file incrementally as rows and rows of pixels are being processed. Because libjpeg is designed to be usable in the lowest-denominator devices (including smart watches, and maybe your refrigerator too?), being able to operate with minimum memory is an important feature.
Sorry but there is no way to tell the size before you make compress the file. If you are not in a hurry, compress the image using different quality values and then select the best one.
I'm having a strange realization while working on a project I'm having.
I created a streaming solution where i stream a image with the resolution 480x640 totaling at 30’720 pixels, and every pixel contains 32bits of data and by my calculations this means that every frame totals to 1,2MB of data which means that 30fps would total to a 36MB/s line.
So to my question how does a streaming solution stream 30fps over f.ex 2mbit/s line?
I'm guessing that the same question can probably used to explain how a jpg image with a 480x640 resolution takes up <100KB
Compression is your friend.
I don't know the specifics of your solution, but a few assumptions can be made.
First off, even if you send each frame as a full frame, they should be compressed. Even lossless compression should get you some pretty good compression rates, but if you go with something lossy (like jpg) then you can get even more.
But that's not all you get. Any good video codec should provide significant compression as well. Parts of the image that don't change between frames don't need to be sent at all, and other parts can be compressed nicely too (I don't know much specifics about the compression used, but there's a lot of stuff that's done to compress it).
This all adds up to a lot of savings over sending a full 32bit bitmap for every frame.
Compression is a very broad topic. Just to get an idea, try reading the wikipedia page about image compression
As a very basic solution to your problem, I would personally jpeg-encode the first frame, then, jpeg-encode the differences between two consecutive frames.
For jpeg compression there are many libraries providing the functionality, without the need to implement it yourself.
If you are not so interested in the quality, you can also subsample the video, for example obtaining frames of resolution 240*320
If we compare image procesing of the losslessly compressed images with the image processing of the lossy compressed images, does the latter provide the results comparable to the former one.
I am asking this question because the images prodiced by lossless compression are ok for human eye but they vary at minute details which may effect the processing of images by the computer. But I can't tell how much.
I don't see much of a question here, but you are right. It is especially visible if processing a JPG image with a medium compression ratio -- the 8x8 squares of which JPG's are built of tend to get more visible after filtering.
This is comparable to the rising of computational error when operating on computer-based floating point numbers.
Your best bet for image processing is using lossless formats for image processing -- PNG's are a good choice, cause they both provide lossless compression, as well as a decent support for bitdepths, transparency and are browser-compatible.
Another format, more often used in the professional world are TIFF's (Targa).
However, note that if your source image is already in a loss-based format, converting it to a lossless one will only prevent adding additional artifact's, not spreading and enhancing the old one. You can however reduce the extent of error by converting it to a lossless format and running it through a small seed gaussian blur.
Perhaps you are looking for the Perceptual Image Diff utility?
This is really a two part question, since I don't fully understand how these things work just yet:
My situation: I'm writing a web app which lets the user upload an image. My app then resizes to something displayable (eg: 640x480-ish) and saves the file for use later.
My questions:
Given an arbitrary JPEG file, is it possible to tell what the quality level is, so that I can use that same quality when saving the resized image?
Does this even matter?? Should I be saving all the images at a decent level (eg: 75-80), regardless of the original quality?
I'm not so sure about this because, as I figure it: (let's take an extreme example), if someone had a 5 megapixel image saved at quality 0, it would be blocky as anything. Reducing the image size to 640x480, the blockiness would be smoothed out and barely less noticeable... until I saved it with quality 0 again...
On the other end of the spectrum, if there was an image which was 800x600 with q=0, resizing to 640x480 isn't going to change the fact that it looks like utter crap, so saving with q=80 would be redundant.
Am I even close?
I'm using GD2 library on PHP if that is of any use
You can view compress level using the identify tool in ImageMagick. Download and installation instructions can be found at the official website.
After you install it, run the following command from the command line:
identify -format '%Q' yourimage.jpg
This will return a value from 0 (low quality, small filesize) to 100 (high quality, large filesize).
Information source
JPEG is a lossy format. Every time you save a JPEG same image, regardless of quality level, you will reduce the actual image quality. Therefore even if you did obtain a quality level from the file, you could not maintain that same quality when you save a JPEG again (even at quality=100).
You should save your JPEG at as high a quality as you can afford in terms of file size. Or use a loss-less format such as PNG.
Low quality JPEG files do not simply become more blocky. Instead colour depth is reduced and the detail of sections of the image are removed. You can't rely on lower quality images being blocky and looking ok at smaller sizes.
According to the JFIF spec. the quality number (0-100) is not stored in the image header, although the horizontal and vertical pixel density is stored.
For future visitors, checking the quality of a given jpeg, you could just use imagemagick tooling:
$> identify -format '%Q' filename.jpg
92%
Jpeg compression algorithm has some parameters which influence on the quality of the result image.
One of such parameters are quantization tables which defines how many bits will be used on each coefficient. Different programs use different quatization tables.
Some programs allow user to set quality level 0-100. But there is no common defenition of this number. The image made with Photoshop with 60% quality takes 46 KB, while the image made with GIMP takes only 26 KB.
Quantization tables are also different.
There are other parameters such subsampling, dct method and etc.
So you can't describe all of them by single quality level number and you can't compare quality of jpeg images by single number. But you can create such number like photoshop or gimp which will describe compromiss between size on quality.
More information:
http://patrakov.blogspot.com/2008/12/jpeg-quality-is-meaningless-number.html
Common practice is that you resize the image to appropriate size and apply jpeg after that. In this case huge and middle images will have the same size and quality.
Here is a formula I've found to work well:
jpg100size (the size it should not exceed in bytes for 98-100% quality) = width*height/1.7
jpgxsize = jpg100size*x (x = percent, e.g. 0.65)
so, you could use these to find out statistically what quality your jpg was last saved at. if you want to get it down to let's say 65% quality and if you want to avoid resampling, you should compare the size initially to make sure it's not already too low, and only then reduce the quality
As there are already two answers using identify, here's one that also outputs the file name (for scanning multiple files at once):
If you wish to have a simple output of filename: quality for use on multiple images, you can use
identify -format '%f: %Q' *
to show the filename + compression of all files within the current directory.
So, there are basically two cases you care about:
If an incoming image has quality set too high, it may take up an inappropriate amount of space. Therefore, you might want, for example, to reduce incoming q=99 to q=85.
If an incoming image has quality set too low, it might be a waste of space to raise it's quality. Except that an image that's had a large amount of data discarded won't magically take up more space when the quality is raised -- blocky images will compress very nicely even at high quality settings. So, in my opinion it's perfectly OK to raise incoming q=1 to q=85.
From this I would think simply forcing a decent quality setting is a perfectly acceptable thing to do.
Every new save of the file will further decrease overall quality, by using higher quality values you will preserve more of image. Regardless of what original image quality was.
If you resave a JPEG using the same software that created it originally, using the same settings, you'll find that the damage is minimized - the algorithm will tend to throw out the same information it threw out the first time. I don't think there's any way to know what level was selected just by looking at the file; even if you could, different software almost guarantees different parameters and rounding, making a match almost impossible.
This may be a silly question, but why would you be concerned about micromanaging the quality of the document? I believe if you use ImageMagick to do the conversion, it will manage the quality of the JPEG for you for best effect. http://www.php.net/manual/en/intro.imagick.php
Here are some ways to achieve your (1) and get it right.
There are ways to do this by fitting to the quantization tables. Sherloq - for example - does this:
https://github.com/GuidoBartoli/sherloq
The relevant (python) code is at https://github.com/GuidoBartoli/sherloq/blob/master/gui/quality.py
There is another algorithm written up in https://arxiv.org/abs/1802.00992 - you might consider contacting the author for any code etc.
You can also simulate file_size(image_dimensions,quality_level) and then invert that function/lookup table to get quality_level(image_dimensions,file_size). Hey presto!
Finally, you can adopt a brute-force https://en.wikipedia.org/wiki/Error_level_analysis approach by calculating the difference between the original image and recompressed versions each saved at a different quality level. The quality level of the original is roughly the one for which the difference is minimized. Seems to work reasonably well (but is linear in the for-loop..).
Most often the quality factor used seems to be 75 or 95 which might help you to get to the result faster. Probably no-one would save a JPEG at 100. Probably no-one would usefully save it at < 60 either.
I can add other links for this as they become available - please put them in the comments.
If you trust Irfanview estimation of JPEG compression level you can extract that information from the info text file created by the following Windows line command (your path to i_view32.exe might be different):
"C:\Program Files (x86)\IrfanView\i_view32.exe" <image-file> /info=txtfile
Jpg compression level is recorded in the IPTC data of an image.
Use exiftool (it's free) to get the exif data of an image then do a search on the returned string for "Photoshop Quality". Or at least put the data returned into a text document and check to see what's recorded. It may vary depending on the software used to save the image.
"Writer Name : Adobe Photoshop
Reader Name : Adobe Photoshop CS6
Photoshop Quality : 7"