Squeezing Images

Squeezing Images

Presented at Uber Destination:Web meetup in San Francisco (https://www.meetup.com/UberEvents/events/247549849/).

The current best practice for choosing an image format is to use JPEG for digital photos and PNG for artwork and company logos. In this talk, we examine these two popular formats in a bit more detail to understand the reasoning behind this conventional wisdom. Armed with this knowledge, we will learn color quantization, low-pass filtering, and aggressive encoding to reduce the size of the images even further.

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Ariya Hidayat

February 28, 2018
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Transcript

  1. 2.
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    Objective Deliver a two-dimensional array of colors in the smallest

    possible PNG/JPEG file without any noticeable loss in visual quality constraint #2 constraint #1
  5. 8.

    010110010101 01011001010101 0100 1001010110010101010100 1001 0101100101010101001001 01011001010101010010010 101 010110010101 01011001010101

    0100 1001010110010101010100 1001 0101100101010101001001 01011001010101010010010 101 Lossless Lossy Not identical vs the original
  6. 9.

    .jpg 8 megapixels JPEG Quality 95 2,210 KB MozJPEG Q

    = 84 879 KB 2 seconds .jpg Guetzli 5 minutes 877 KB
  7. 13.

    Spatial → Frequency Domain 40 24 15 19 28 24

    19 15 38 34 35 35 31 28 27 29 40 47 49 40 33 29 32 43 42 49 50 39 34 30 32 46 40 47 46 35 31 32 35 43 38 43 42 31 27 27 28 33 39 33 25 17 14 15 19 26 29 16 6 1 -4 0 7 18 239 32 27 -12 3 -5 3 1 34 -3 -19 6 3 0 -1 1 -70 2 8 23 9 6 -1 -1 5 0 -6 11 -2 0 -1 1 -17 -3 6 6 3 -1 0 0 2 4 2 2 1 -2 0 1 -3 0 0 -1 -1 -1 0 0 1 -1 3 1 0 0 0 0 Discrete Cosine Transform (DCT)
  8. 15.

    Quantization of DCT Coefficients 92 3 -9 -7 3 -1

    0 2 -39 -58 12 17 -2 2 4 2 -84 62 1 -18 3 4 -5 5 -52 -36 -10 14 -10 4 -2 0 -86 -40 49 -7 17 -6 -2 5 -62 65 -12 -2 3 -8 -2 0 -17 14 -36 17 -11 3 3 -1 -54 32 -9 -9 22 0 1 3 90 0 -7 0 0 0 0 0 -35 -56 9 11 0 0 0 0 -84 54 0 -13 0 0 0 0 -45 -33 -0 0 0 0 0 0 -77 -39 45 0 0 0 0 0 -52 60 0 0 0 0 0 0 -15 0 -19 0 0 0 0 0 -51 19 0 0 0 0 0 0 Affected by the Q factor https://cs.stanford.edu/people/eroberts/courses/soco/projects/data-compression/lossy/jpeg/coeff.htm
  9. 16.

    Reduce Quality, Lose Details github.com/mozilla/mozjpeg > cjpeg –quality 84 original.jpg

    > output.jpg 2,210 KB → 879 KB 8 megapixels Original Q=95
  10. 17.

    Alternative JPEG Encoder: Guetzli > guetzli –-quality 84 original.jpg output.jpg

    github.com/google/guetzli 2,210 KB → 877 KB 8 megapixels Original Q=95
  11. 23.

    Portable Network Graphics Lossless encoding Truecolor RGB RGB+A Indexed Color

    Grayscale RFC 2083 http://www.w3.org/TR/PNG/ Good compression (using DEFLATE) 8 bits/channel
  12. 25.

    Lempel-Ziv: De-duplication 'Cause the players gonna play, play, play, play,

    play 'Cause the players gonna play, play, play, play, play 'Cause the players gonna[5:14], play, play, play, play 'Cause the players gonna[5:14], [6:6]play, play, play
  13. 26.
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    Duplication Search Trade-offs 'Cause the players gonna play, play, play,

    play, play 'Cause the players gonna[5:14], [6:6] [6:6] [6:6] [6:6] 'Cause the players gonna[5:14], [6:6] [12:12] [6:6]
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    Zopfli: Exhaustive DEFLATE https://ariya.io/2016/06/using-zopfli-to-optimize-png-images github.com/google/zopfli > zopflipng tiger.png tiger-small.png Optimizing

    tiger.png Input size: 197220 (192K) Result size: 135325 (132K). Percentage of original: 68.616% Result is smaller
  16. 36.
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    pngquant.org > pngquant --speed 1 --verbose tiger.png tiger.png: read 193KB

    file made histogram...9734 colors found selecting colors...2% selecting colors...4% selecting colors...17% selecting colors...29% selecting colors...31% selecting colors...44% selecting colors...57% selecting colors...70% selecting colors...82% selecting colors...95% selecting colors...100% moving colormap towards local minimum eliminated opaque tRNS-chunk entries...59 entries transparent mapped image to new colors...MSE=1.048 (Q=96) writing 256-color image as tiger-fs8.png Quantized 1 image.
  18. 40.