JPEG is a lossy image compression which can give a high compression ratio.
As far as I know, information loss takes place in JPEG during quantization.
Are there any other steps in JPEG compression where the loss takes place or can take place?
If it takes place, then where?
There are 3 aspects of JPEG compression which affect the quality and accuracy of images:
1) Loss of precision takes place during the quantization stage. Accuracy of the colors is lost in order to reduce the amount of data generated.
2) Errors are introduced during the conversion to/from the RGB/YCC color spaces.
3) Errors are introduced during the transformation to/from the frequency domain. The Discrete Cosine Transform converts pixels into the frequency domain. This conversion incurs errors in both directions.
Another place where loss can take place in JPEG compression is the chroma subsampling stage.
My understanding is that most JPEG-compressed images use 4:2:0 color subsampling: after converting each pixel from RGB to YCbCr, the Cb values for a 2x2 block of pixels are averaged to a single value, and the Cr values for that 2x2 block of pixels are also averaged to a single value.
The JPEG standard also supports 4:4:4 (no downsampling).
Related
According to this site
BMP (Bitmap) is a uncompressed raster graphics image format
1) So does it mean that, BMP doesn't follow compression while image storing at all ?
2) If it does follow compression then it should be called lossy ? But it's lossless why so ?
Also when it is said,
Lossless means that the image is made smaller, but at no detriment to
the quality
3) If the image is made smaller then how can it remain the same.Making it smaller means that it has to follow some compression right?
Edit:
4) JPEG is also a bitmap format then why is it not lossless ?
First, BMP does not allow image compression at all, pixel values are written as is, no compression or size reduction transformation is used. It's uncompressed, so it's not lossy, it's lossless. It is actually possible to compress images (and also audio) in a lossless manner, that is, mathematical operations are performed on the data that remove redundant data, thus decreasing the overall size, since these operations are invertible they are also able to recover the original data (image, audio, etc...). Technically, a bitmap is a 2 dimensional array of pixel values, but a bitmap is widely known as the uncompressed .bmp image format. Compression has two variants, lossy compression, where you drop some portion of your data that can't be recovered, hence lossy; and lossless, where you drop portions of your data that can be recovered by the inverse process. A full treatment on the subject inevitably has to deal with information theory and Shannon's result on coding theory. A simple place to start is with run-length enconding and Lempel-Ziv compression algorithm for lossless compression, and JPEG compression using wavelets for lossy compression.
My team wish to calculate the contrast between two photographs taken in a wet environment.
We will calculate contrast using the formula
Contrast = SQRT((ΔL)^2 + (Δa)^2 + (Δb)^2)
where ΔL is the difference in luminosity, Δa is the difference in (redness-greeness) and Δb is (yellowness-blueness), which are the dimensions of Lab space.
Our (so far successful) approach has been to convert each pixel from RGB to Lab space, and taking the mean values of the relevant sections of the image as our A and B variables.
However the environment limits us to using a (waterproof) GoPro camera which compresses images to JPEG format, rather than saving as TIFF, so we are not using a true-colour image.
We now need to quantify the uncertainty in the contrast - for which we need to know the uncertainty in A and B and by extension the uncertainties (or mean/typical uncertainty) in each a and b value for each RGB pixel. We can calculate this only if we know the typical/maximum uncertainty produced when converting from true-colour to JPEG.
Therefore we need to know the maximum possible difference in each of the RGB channels when saving in JPEG format.
EG. if true colour RGB pixel (5, 7, 9) became (2, 9, 13) after compression the uncertainty in each channel would be (+/- 3, +/- 2, +/- 4).
We believe that the camera compresses colour in the aspect ratio 4:2:0 - is there a way to test this?
However our main question is; is there any way of knowing the maximum possible error in each channel, or calculating the uncertainty from the compressed RGB result?
Note: We know it is impossible to convert back from JPEG to TIFF as JPEG compression is lossy. We merely need to quantify the extent of this loss on colour.
In short, it is not possible to absolutely quantify the maximum possible difference in digital counts in a JPEG image.
You highlight one of these points well already. When image data is encoded using the JPEG standard, it is first converted to the YCbCr color space.
Once in this color space, the chroma channels (Cb and Cr) are downsampled, because the human visual system is less sensitive to artifacts in chroma information than it is lightness information.
The error introduced here is content-dependent; an area of very rapidly varying chroma and hue will have considerably more content loss than an area of constant hue/chroma.
Even knowing the 4:2:0 compression, which describes the amount and geometry of downsampling (more information here), the content still dictates the error introduced at this step.
Another problem is the quantization performed in JPEG compression.
The resulting information is encoded using a Discrete Cosine Transform. In the transformed space, the results are again quantized depending on the desired quality. This quantization is set at the time of file generation, which is performed in-camera. Again, even if you knew the exact DCT quantization being performed by the camera, the actual effect on RGB digital counts is ultimately content-dependent.
Yet another difficulty is noise created by DCT block artifacts, which (again) is content dependent.
These scene dependencies make the algorithm very good for visual image compression, but very difficult to characterize absolutely.
However, there is some light at the end of the tunnel. JPEG compression will cause significantly more error in areas of rapidly changing image content. Areas of constant color and texture will have significantly less compression error and artifacts. Depending on your application you may be able to leverage this to your benefit.
I've done a bit of fiddling around with PNGs over the last couple days and I am upset with my findings. I'm concluding that the majority of my results deal with compression. So this weekend I'm going to dive into advanced compression articles. I wanted to share my findings so far. To see if anyone has any advice on achieving my goal and to maybe point me in the right direction.
I am currently working on a project where I need to obtain the smallest possible file size within a window of less than 15 seconds.
The majority of the images I am working with are PNG-8bpp with a full 256 color palette. Most of these images I could represent accurately with 5bpp (32 colors).
PNG indexed however only supports 1,2,4, and 8bpp. So my idea was to strip the PNG format to the minimal information I needed and write an encoder/decoder to support IDAT sections with 3,5,6, or 7bpp.
Test 1:
Original File: 61.5KB, 750 * 500, 8pp Palette, 256 colors, No tRNS
After Optimizations (Reductions to 4bpp, Strip Anx Chunks, & PNGOUT): 49.2KB 4bpp, 16 Colors
Human Interpretation: I can see 6 distinguishable colors.
Since I only need six colors to represent the image I decide to encode the IDAT using 3bpp to give me a max palette of 8 colors. First I uncompressed the IDAT which results in a new file size of 368KB. After applying a 3bpp to IDAT my new uncompressed file size is 274KB. I was off to what seemed to be a good start... Next I applied deflate to my new IDAT section. Result... 59KB.
10KB larger than using 4bpp.
Test 2:
Original File: 102KB, 1000 * 750, 8bpp, 256 Colors, tRNS 1 fully transparent color
After Optimization: 79KB, 8bpp, 193 colors, tRNS 1 full transparent color
Human Interpretation: I need about 24 colors to represent this picture.
24 colors can be represented in 5bpp at 32 colors. Using the same technique above I was able to achieve much better results over uncompressed but again I lost at compression. Final size compressed... 84KB. Then I tried at 6,7bpp... same result poorer compression that at 8bpp.
Just to be sure I saved all the uncompressed images and tried several other compression algorithms... LZMA, BZIP2, PAQ8... same result smaller compression size at 8bpp than at 5,6, or 7bpp AND smaller size at 4bpp than at 3bpp.
Why is this occuring? Can I tweak/modify a compression algorithm to target a PNG like format that uses a 5,6, or 7bpp format that beast 8bpp compression? Is it worth the time... and yes saving another 10KB would be worth it.
What you're seeing is that by using odd pixel sizes, your effective compression decreases because of the way PNG compression works. The advantage of PNG compression over just using straight FLATE/ZIP compression is the filtering. PNG compression tries to exploit horizontal and vertical symmetry with a small assortment of pre-processing filters. These filters work on byte boundaries and are effective with pixel sizes of 4/8/16/24/32/48/64 bits. When you move to an odd size pixel (3/5/6/7 bits) you are defeating the filtering because identically colored pixels won't "cancel each other out" horizontally when filtered on 8-bit boundaries.
Even if the filtering weren't an issue, the way that FLATE compression works, reducing the pixel size from 8 to 7 or 6 bits won't have much effect either because it also assumes a symbol size of 8-bits.
In conclusion...the only benefit you can achieve by using odd sizes of pixels is that the uncompressed data will be smaller. By breaking the pixels' byte boundary symmetry, you defeat much of the benefit of PNG compression.
GIF compression supports all pixel sizes from 1 to 8 bits. It defines the symbol size as the pixel size and doesn't use any pre-filtering. An 8-bit GIF image, if compressed as 7-bit pixels, wouldn't suffer less compression, but also wouldn't benefit because the compression depends more on the repetition of the pixels than the symbol size.
DEFLATE compression used by PNG has two main techniques:
finds repeating byte sequences and encodes them as backreferences
encodes bytes using Huffman coding
By changing pixel length from 8-bit you're out of sync with byte boundaries and DEFLATE won't be able to encode repeating pixel runs as repeated bytes.
And thanks to Huffman coding it doesn't matter that 8-bit pixels have unused bits, because the coding will encode bytes with variable-width codes assigning shortest ones to most frequently occurring values.
A JPEG image, if it is non-progrssive loads from top-to-bottom, and not from left-to-right or any other manner.
Doesn't that imply that jpeg uses some row-wise compression technique? Does it (use a row-wise compression technique)?
No, it doesn't. JPEG mainly constitutes the use of chroma channel subsampling, a discrete cosine transform (DCT) and some non-lossy compression such as run-length encoding. The image is divided into blocks, usually 8x8 pixels, and then transformed into a frequency domain representation via DCT. In a non-progressive JPEG, these blocks would be stored left to right, top to bottom. With a progressive JPEG, the lower frequency components will be stored before the higher ones, allowing a low-resolution preview be viewed before the whole image has been transmitted.
As you can rotate a JPEG by 90 degrees quick and lossless I think it's not row-major compression. It's just that the compressed blocks are stored in some order and that happens to be row by row.
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.