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https://github.com/rflynn/imgmin

Lossy image optimization
https://github.com/rflynn/imgmin

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Lossy image optimization

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README 

        
imgmin

======



Get Started!

------------

    sudo apt-get install -y autoconf libmagickwand-dev pngnq pngcrush pngquant

    git clone https://github.com/rflynn/imgmin.git

    cd imgmin

    autoreconf -fi

    ./configure

    make

    sudo make install

    imgmin original.jpg optimized.jpg



Summary

-------

Image files constitute a majority of static web traffic.[17]

Unlike text-based web file formats, binary image files do not benefit from

built-in webserver-based HTTP gzip compression.

imgmin offers an automated means for enforcing image quality as a

standalone tool and as a webserver module.

imgmin determines the optimal balance of image quality and filesize, often

greatly reducing image size while retaining quality for casual use, which

translates into more efficient use of storage and network bandwidth, which

saves money and improves user experience.



The Problem

-----------

Websites are composed of several standard components.

Most (HTML, CSS, Javascript, JSON, XML, etc) are text-based.

They can be efficiently compressed for transfer via gzip, supported by all

mainstream webservers and browsers.

But image and video files are binary, non-text files, and generally are not

worth auto-compressing in the webserver.



Most web traffic consists of image file downloads, specifically JPEG images.

JPEG files use so much bandwidth that Google has tried improving them by

introducing an alternative format[16].

JPEG images are not compressed by the webserver because JPEG is a binary format

which does not compress well because it includes its own built-in compression,

and generally it is up to the people creating the images to select an appropriate

compression setting when the file is saved.  



The JPEG quality settings most used by graphics professionals tend to be highly

conservative because Compression and image quality are inversely proportional

and graphics people are interested in utmost visual quality and not in spending

time worrying about network efficiency.



The result of overly conservative JPEG compression and webservers' inability

to compress them any further means that many images on the web are too large.

JPEG's overwhelming popularity as the most common image format means that many

pages contain dozens of JPEG images.



These bloated images take longer to transfer, leading to extended load time,

which does not produce a good viewer experience. People hate to wait.



In order to understand how to optimize JPEGs for size first we must learn more

about the JPEG format.



"Quality" Details

-----------------

JPEG images contain a single setting usually referred to as "Quality",

and it is usually expressed as a number from 1-100, 100 being the highest.

This knob controls how aggressive the editing program is when saving the

file. A lower quality setting means more aggressive compression, which

generally leads to lower image quality. Many graphics people are hesitant to

reduce this number below 90-95.



But how exactly does "quality" affect the image visibly? Does the same

image at quality 50 look "half as good" than quality 100? What does half

as good even mean, anyway? Can people tell the difference between an image

saved at quality 90 and quality 89? And how much smaller is an image saved

at a given quality? Is the same image at quality 50 half as large as at 100?



Here is a chart of the approximate relationship between the visual effect of

"quality" and the size of the resulting file.



    100% |#*******

     90% | #      ******* Visual Quality (approximate)

     80% |  #            ********

     70% |   #                   ********    --- noticeably worse at some point ---

     60% |    ##                         *******

     50% |      ###                             ******

     40% |         #####                              ****

     30% |    File Size ######                            ***

     20% |                    ################               *****

     10% |                                    ####################******

      0% +---------------------------------------------------------------

         100    90    80    70     60    50    40     30     20    10    0



The precise numbers vary for each image, but the convex shape of the "Visual

Quality" curve and the concave "File Size" curve hold for each image. This is

the key to reducing file size.



For an average JPEG there is a very minor, mostly insignificant change in

*apparent* quality from 100-75, but a significant filesize difference for

each step down. This means that many images look good to the casual viewer

at quality 75, but are half as large than they would be at quality 95. As

quality drops below 75 there are larger apparent visual changes and reduced

savings in filesize.



Even More Detail

----------------

So, why not just force all JPEGs to quality 75 and leave it at that?



Some sites do just that:



    Google Images thumbnails:  74-76

    Facebook full-size images: 85

    Yahoo frontpage JPEGs:     69-91

    Youtube frontpage JPEGs:   70-82

    Wikipedia images:          80

    Windows live background:   82

    Twitter user JPEG images:  30-100, apparently not enforcing quality



This is a fine strategy and is low-risk, straight-forward and inexpensive.



But for optimal results it is not that simple. Compression results rely heavily

on the data being compressed. This means that visual quality is not uniform for

all images at a given quality setting. Imposing a single quality, no matter

what it is, will be too low for some images, resulting in poor visual quality

and will be too high for others, resulting in wasted space.



So we are left with a question:



**What is the optimal quality setting for a given image with regard to filesize

but still remain indistinguishable from the original?**



The widely accepted answer, as formulated by the 'JPEG image compression FAQ'[5]:



**This setting will vary from one image to another.**



So, there is no one setting that will save space but still ensure that images

look good, and there's no direct way to predict what the optimal setting is for

a given image.



Looking For Patterns

--------------------

Based on what we know, the easiest way around our limitations would be to

generate multiple versions of an image in a spectrum of qualities and have

a human choose the lowest quality version of the image of acceptable quality.



I proceded in this way for a variety of images, producing an interactive image

gallery. Along with each image version I included several statistical measures

available from the image processing library and a pattern emerged.



Given a high quality original image, apparent visual quality began to diminish

noticably when mean pixel error rate exceeded 1.0.



This metric measures the amount of change, on average, each pixel in the new

image is from the original. Specifically, JPEGs break image data into 8x8 pixel

blocks. The quality setting controls the amount of information available

to encode quantized color and brightness information about a block. The less

space available to store each block's data the more distorted and pixelated

the image becomes -- you can verify this by inspecting an image saved

at quality 0 -- each 8x8 block of pixels should be assigned a single color.



The change in pixel error rate is not directly related to the quality setting,

again, an image's ultimate fate lies in its data; some images degrade rapidly

within 1 or 2 quality steps, while others compress with little visible

difference from quality 95 to quality 50.



Automating the Process

----------------------

Given the aforementioned observation of high-quality images looking similar

within a mean pixel error rate of 1.0, the method of determining an optimal

quality setting for any given JPEG is clear: generate versions of an image at

multiple different quality settings, and find the version with the mean pixel

error rate nearest to but not exceeding 1.0.



Using quality bounds of [95, 50] we perform a binary search of the quality

space, converging on the lowest quality setting that produces a mean pixel

error rate of < 1.0.



For general-purpose photographic images with high color counts the above method

yields good results in tests.



Limitations

-----------

One notable exception is in low color JPEG images, such as gradients and low-

contrast patterns used in backgrounds. The results at ~1.0 are often unacceptably

pixelated. Our image-wide statistical measure is not "smart" enough to catch

this, so currently images with < 4096 colors are passed through unchanged.

For reference the "google" logo on google.com contains 6438 colors. In practice

this is not a problem for a typical image-heavy website because there are

relative few layout-specific "background" graphics which can be (and are) handled

separately from the much larger population of "foreground" images.



Implementation

--------------

The implementation for the standalone client and apache module is in C.

The original script is in Perl.

The interactive image gallery in web/ uses PHP.

All use the excellent ImageMagick graphics library.



Performance

-----------

0.5-2 seconds for a typical image on a typical 2015 machine.

Automatically scales to multiple CPUs via Imagemagick's built-in OpenMP support.



Conclusion

----------

In conclusion I have created an automated method for generating optimally-sized

JPEG images for casual use that can be integrated into existing workflows.

The method is low cost to deploy and run and can yield appreciable and direct

benefits in the form of improving webserver efficiency, reducing website latency,

and most importantly improving overall viewer experience.

This method is generally applicable and can be applied to any collection of or

website containing JPEG images.



References

==========

  1. "JPEG" Wikipedia, The Free Encyclopedia. Wikimedia Foundation, Inc. 3 July 2011. Web. 7 Jul. 2011.

     

  2. "Joint Photographic Experts Group" Wikipedia, The Free Encyclopedia. Wikimedia Foundation, Inc. 29 June 2011. Web. 7 Jul. 2011.

     

  3. "Information technology – Digital compression and coding of continuous-tone still images – Requirements and guidelines" 1992. Web. 7 Jul. 2011

     

  4. "Independent JPEG Group" 16 Jan. 2011 Web 7 Jul. 2011

      

  5. "JPEG image compression FAQ" Lane, Tom et. al. 28 Mar. 1999 Web. 7 Jul. 2011

     

  6. "JPEG Discrete cosine transform". Wikipedia, The Free Encyclopedia. Wikimedia Foundation, Inc. 3 July 2011. Web. 7 Jul. 2011.

     

  7. "GetImageQuantizeError()" ImageMagick Studio LLC. Revision 4754 [computer program]

      (Accessed July 7 2011)

  8. "A Color-based Technique for Measuring Visible Loss for Use in Image Data Communication" Melliyal Annamalai, Aurobindo Sundaram, Bharat Bhargava 1996. Web 10 Jul 2011

     

  9. "An Evaluation of Transmitting Compressed Images in a Wide Area Network" Melliyal Annamalai, Bharat Bhargava 1995. Web 10 Jul 2011

     

  10. "ImageMagick v6 Examples -- Common Image Formats: JPEG Quality vs File Size" ImageMagick Studio LLC

     

  11. "JPEG Compression, Quality and File Size" ImpulseAdventure.com, Calvin Hass

     

  12. "Designing a JPEG Decoder & Source Code" ImpulseAdventure.com, Calvin Hass

     

  13. "JPEG Compression" Gernot Hoffmann. 18 Sep 2003. Web. 13 Aug 2011

     

  14. "Optimization of JPEG (JPG) images: good quality and small size" Alberto Martinez Perez. 16 Sep 2008. Web. 14 Aug 2011

     

  15. "JPEG: Joint Photographic Experts Group"

     

  16. "WebP: A new image format for the Web", Google, 2012. Web. 31 Jan 2012.

     

  17 "New WebP Image Format Could Send JPEG Packing", Rob Spiegel, 10 Oct 2010. Web. 31 Jan 2012

     



Technical Notes

===============



License

-------

    This software is licensed under the MIT license.

    See LICENSE-MIT.txt and/or http://www.opensource.org/licenses/mit-license.php



Installation

------------



### Prerequisites



On Ubuntu Linux via `apt-get`:

    

    $ sudo apt-get install imagemagick libgraphicsmagick1-dev libmagickwand-dev perlmagick apache2-prefork-dev



On Redhat Linux via `yum`:

    

    $ sudo yum install Imagemagick ImageMagick-devel Perlmagick apache2-devel



On Unix via source:

    

    $ cd /usr/local/src                                                # source directory of choice

    $ sudo wget -nH -nd ftp://ftp.imagemagick.org/pub/ImageMagick/ImageMagick.tar.gz

    $ sudo gzip -dc ImageMagick-6.7.1-3.tar.gz | sudo tar xvf -        # extract

    $ cd ImageMagick-6.7.1-3                                           # change dir

    $ sudo ./configure                                                 # configure

    $ sudo make -j2                                                    # compile

    $ sudo make install                                                # install



imgmin



    from source: see top of README



Examples

--------



### Generic



    $ time ./src/imgmin examples/Afghan-Girl-by-Steve-McCurry.jpg Afghan-Girl-by-Steve-McCurry-after.jpg

    Before quality:85 colors:44904 size: 58.8kB type:TrueColor format:JPEG 0.27/0.06@72 0.36/0.08@66 0.42/0.10@63 0.44/0.06@61

    After  quality:61 colors:51650 size: 29.6kB saved: 29.2kB (49.7%)



### My 2014 Macbook



    $ time imgmin examples/lena1.jpg examples/lena1-after.jpg

    Before quality:92 colors:73822 size: 89.7kB type:TrueColor format:JPEG 0.93/0.02@76 1.13/0.02@68 1.01/0.02@72

    After  quality:72 colors:74073 size: 35.8kB saved: 53.8kB (60.0%)



    real    0m0.696s

    user    0m0.590s

    sys     0m0.060s