The document discusses the concept of image compression using q_auto and f_auto parameters to intelligently deliver optimized images based on context and device capabilities. It highlights the importance of adapting image formats and quality to reduce bandwidth while maintaining perceptual quality, detailing various encoding techniques and algorithms used. The conclusion emphasizes that this approach minimizes compression artifacts and improves overall performance in image delivery.

1. q_auto,f_auto
A peek under the hood
IMAGE COMPRESSION:
GRACEFUL DEGRADATION
FOR BETTER PERFORMANCE
Jon Sneyers
October 2016

2. AUTO EVERYTHING: G_AUTO,W_AUTO,DPR_AUTO,Q_AUTO,F_AUTO
DELIVER THE RIGHT IMAGE TO EVERYONE
▸ Different crops depending on context (g_auto,w_auto)
Responsive design
Android tablet
185K WebP
1500x844 pixels
Firefox desktop
381K JPEG
2000x1125 pixels
iPhone
54K JPEG
500x500 pixels

3. AUTO EVERYTHING: G_AUTO,W_AUTO,DPR_AUTO,Q_AUTO,F_AUTO
DELIVER THE RIGHT IMAGE TO EVERYONE
Big Android phone
High bandwidth
144K WebP
1000x1000 pixels
Windows phone
142K JPEG XR
1000x1000 pixels
iPhone
54K JPEG
500x500 pixels
Retina iPhone
201K JPEG
1000x1000 pixels
Small Android phone
Low bandwidth
43K WebP
500x500 pixels
Topic of this webinar
▸ Same image, different encoding depending on image and context
(q_auto,f_auto,dpr_auto)

4. Q_AUTO,F_AUTO - INTRO
FIXED QUALITY, FIXED FORMAT
▸ Common practice: fixed format, fixed quality, fixed encode settings
▸ E.g. WordPress default: re-encode as JPEG quality 82
▸ E.g. Facebook, Twitter, Instagram: re-encode as low-quality JPEG
▸ This is suboptimal for three reasons:
▸ Optimal quality depends on the image: some images look fine at JPEG
quality 50, others have visible compression artifacts at quality 85
▸ Optimal quality/size trade-off depends on context
(viewing device, network conditions, …)
▸ Best format depends on 1) image, and 2) receiver decode capabilities

5. Q_AUTO,F_AUTO - INTRO
INTRODUCING Q_AUTO,F_AUTO
▸ Automatic quality, automatic image format
▸ q_auto:low, q_auto:eco, q_auto:good, q_auto:best
▸ Deliver the optimal file to every browser/device
▸ Most suitable format is picked among all formats the receiver accepts
(Accepts header), can depend on the image
▸ Suitable quality settings are picked automatically
▸ Depending on the image content
▸ Save-Data client hint lowers the target quality
(from q_auto:good to q_auto:eco)

6. Q_AUTO,F_AUTO - TARGET FORMATS
TARGET IMAGE FORMATS
▸ JPEG
▸ All browsers, only opaque images (no alpha channel supported)
▸ PNG
▸ All browsers, not very useful for opaque photographs
▸ WebP
▸ Chrome/Opera/Android only, useful for all kinds of images
▸ WDP (JPEG XR)
▸ IE/Edge only, mostly useful for photographs

7. Q_AUTO,F_AUTO - TARGET FORMATS
LOSSY IMAGE COMPRESSION BASICS
▸ Luma (Y or L) and Chroma (U,V or Cb,Cr or Co,Cg or a,b)
▸ Chroma subsampling
4:4:4 vs 4:2:0

8. Q_AUTO,F_AUTO - TARGET FORMATS
LOSSY IMAGE COMPRESSION BASICS
▸ Discrete Cosine Transform (DCT)
LOW
FREQUENCY
NOTTOOLOSSY
HIGHFREQUENCY
VERYLOSSY

9. Q_AUTO,F_AUTO - TARGET FORMATS
JPEG ENCODE PARAMETERS TO DECIDE
▸ Quality (amount of DCT quantization)
▸ Separate quality setting for luma and chroma
▸ Chroma subsampling: 4:4:4 vs 4:2:0
▸ or “in between” (4:4:4 with lower quality for chroma than luma)
▸ MozJPEG vs libjpeg-turbo
▸ Progressive (smaller, previews) vs non-progressive (faster to decode)
▸ or “in between” (custom progressive scan script)

10. Q_AUTO,F_AUTO - TARGET FORMATS
LOSSY PNG? SURE!
▸ PNG is a lossless format, but it can be used in a lossy way
▸ Two options:
▸ Quantize to PNG8
▸ Maximum 256 RGBA colors
▸ Can use fewer than 256 colors for better compression
▸ Apply a lossy PNG24/32 transformation (diagonal blur)

11. Q_AUTO,F_AUTO - TARGET FORMATS
WEBP: ACTUALLY TWO IMAGE FORMATS
▸ Lossy WebP (VP8 intra-frame)
▸ Always 4:2:0 chroma subsampling (can be problematic)
▸ Usually better than JPEG but certainly not always
▸ Supports alpha
▸ Lossless WebP
▸ Mostly similar to PNG but better compression
▸ Decisions: lossy or lossless? which quality?
or fall back to JPEG or PNG?

12. Q_AUTO - ALGORITHM
THE Q_AUTO ALGORITHM: OVERVIEW
▸ Heuristics that examine the original pixels
▸ Trial encodes
▸ Perceptual metric and heuristics to score trial encodes
▸ Heuristics to select the best format and to decide the actual
encode settings based on the above information
▸ Important constraint: it has to be reasonably fast if we want
to deploy it at “Cloudinary scale”. Latency and cpu cost has
to be reasonable.

13. Uncompressed Image
(no alpha channel)
NON-PHOTOGRAPHIC?
Photographic
Non-photographic
(text, line art, screenshot, …)
CHROMA SUBSAMPLING?
TWO TRIAL JPEG ENCODINGS
(4:4:4 AND 4:2:0)
Y-DSSIM SCORE
Target quality
(low, eco, good,best)
TRIAL LOSSLESS
PNG/WEBP
LIBJPEG 4:2:0
ENCODING
PNG ENCODING
(PNGQUANT)
LOSSLESS
WEBP
LOSSY
WEBP
ANALYSIS ADJUSTING QUALITY
ENCODING
LIBJPEG 4:4:4
ENCODING
MOZJPEG 4:2:0
ENCODING
Too large
compared to
trial JPEG
very bad score:
encode another trial JPEG
at higher quality
Few colors

14. Uncompressed Image
with alpha channel
NON-PHOTOGRAPHIC?
Non-photographic
(text, line art, screenshot, …)
CHROMA SUBSAMPLING?
TWO TRIAL JPEG ENCODINGS
(4:4:4 AND 4:2:0)
Y-DSSIM SCORE
TRIAL LOSSLESS
WEBP
LIBJPEG 4:2:0
ENCODING
PNG ENCODING
(PNGQUANT)
LOSSLESS
WEBP
LOSSY
WEBP
ANALYSIS ADJUSTING QUALITY
ENCODING
LIBJPEG 4:4:4
ENCODING
MOZJPEG 4:2:0
ENCODING
Photographic
Too large
compared to
trial JPEG
Possible, but not useful
if WebP can be used
JPEG does not support alpha
Target quality
(low, eco, good,best)
Uncompressed Image
with alpha channel
very bad score:
encode another trial JPEG
at higher quality

15. Uncompressed Image
with alpha channel
LIBJPEG 4:2:0
ENCODING
PNG ENCODING
(PNGQUANT)
LOSSLESS
WEBP
LOSSY
WEBP
ANALYSIS ADJUSTING QUALITY
ENCODING
LIBJPEG 4:4:4
ENCODING
MOZJPEG 4:2:0
ENCODING
Target quality
(low, eco, good,best)

16. Q_AUTO - ALGORITHM
PHOTOGRAPHIC OR NON-PHOTOGRAPHIC?
Original: 634K PNG
(lossless)
71K JPEG
(lossy)
204K PNG
(lossy)👍 👎

17. Q_AUTO - ALGORITHM
PHOTOGRAPHIC OR NON-PHOTOGRAPHIC?
Original: 221K PNG
(lossless)
97K JPEG
(lossy)
50K PNG
(lossy) 👍👎

18. Q_AUTO - ALGORITHM
CHROMA SUBSAMPLING?
▸ Compute a map of “chroma-related compression artifacts”
▸ A lot of artifacts? Use 4:4:4 with high chroma quality
▸ Don’t use lossy WebP in this case
▸ Some artifacts? Use 4:4:4 with lower chroma quality
▸ (Almost) no artifacts? Use 4:2:0
NON-PHOTOGRAPHIC?
CHROMA
SUBSAMPLING?
Y-DSSIM SCORE
TRIAL LOSSLESS
PNG/WEBP
LIBJPEG PNG
LOSSLESS
WEBP
LOSSY
WEBPMOZJPEG

19. Q_AUTO - ALGORITHM
CHROMA SUBSAMPLING?
Original: 505K PNG
(lossless)
65K JPEG
(q_75, 4:4:4)
57K JPEG
(q_75, 4:2:0) 👍👎

20. Q_AUTO - ALGORITHM
CHROMA SUBSAMPLING?
Original image
(lossless)
4:4:4 JPEG
(lossy, q_100 and q_75)
4:2:0 JPEG
(lossy, q_100 and q_75) 😱

21. Q_AUTO - ALGORITHM
PERCEPTUAL METRIC: Y-DSSIM
▸ Compute multi-scale structural similarity metric (SSIM)
between uncompressed and trial JPEG
▸ Adjust quality accordingly (repeat if needed)
▸ Only look at luma (Y)
▸ No need to look at chroma, chroma subsampling test
already covers that NON-PHOTOGRAPHIC?
CHROMA
SUBSAMPLING?
Y-DSSIM SCORE
TRIAL LOSSLESS
PNG/WEBP
LIBJPEG PNG
LOSSLESS
WEBP
LOSSY
WEBPMOZJPEG

22. Q_AUTO - JPEG ENCODING
ACTUAL JPEG ENCODE
▸ Use either MozJPEG or libjpeg-turbo (depending on quality parameters)
▸ Almost always use 5-scan “semi-progressive” scan script
▸ Faster encode/decode than default (~10 scans) progressive, still
most of the benefits
▸ Only use non-progressive for
▸ very small images
(no advantage)
▸ very large images
(receiver might not have the memory)
NON-PHOTOGRAPHIC?
CHROMA
SUBSAMPLING?
Y-DSSIM SCORE
TRIAL LOSSLESS
PNG/WEBP
LIBJPEG PNG
LOSSLESS
WEBP
LOSSY
WEBPMOZJPEG

23. Q_AUTO - JPEG ENCODING
JPEG - DECODE TIME
▸ JPEG decode speed:
▸ Non-progressive: ~215 MP/s
▸ Default progressive: ~111 MP/s
▸ Semi-progressive: ~184 MP/s
▸ Total decode time has a (minor) effect on time-to-render, in particular
if the image is cached locally
▸ If transfer is the bottleneck, (semi-)progressive has the
advantage of progressive previews and smaller total size

24. Q_AUTO - PNG ENCODING
ACTUAL PNG ENCODE
▸ Use pngquant to quantize colors & improve compression
▸ Use PNG8 if the error is not too bad
▸ The number of colors is picked to reach a specific MSE
▸ Otherwise use lossy PNG24/32 (rarely needed)
▸ Quantize not the pixel values, but the filtered pixel values w.r.t. the
‘average’ predictor ((left+up)/2)
▸ Slight diagonal blur,
no quantization/dithering artifacts
▸ Then use optipng to optimize further
NON-PHOTOGRAPHIC?
CHROMA
SUBSAMPLING?
Y-DSSIM SCORE
TRIAL LOSSLESS
PNG/WEBP
LIBJPEG PNG
LOSSLESS
WEBP
LOSSY
WEBPMOZJPEG

25. Q_AUTO - PNG ENCODING
COLOR QUANTIZATION
Original PNG24, 573KB 256 colors, 203KB 100 colors, 148KB
30 colors, 98KB 5 colors, 39KB 3 colors, 24KB

26. Q_AUTO - PNG ENCODING
SOMETIMES PNG8 IS TOO LOSSY
▸ Original: 88,358 bytes
▸ PNG8: 21,739 bytes
▸ Too lossy: banding / visible dither
▸ Lossy PNG32: 57,552 bytes
▸ 53,101 bytes at q_auto:eco
▸ 64,771 bytes at q_auto:best

27. Q_AUTO - PNG ENCODING
SOMETIMES PNG8 IS TOO LOSSY
▸ Original: 88,358 bytes
▸ PNG8: 21,739 bytes
▸ Lossy PNG32: 53,101 bytes

28. EVALUATION
Q_AUTO

29. Q_AUTO - EVALUATION
EVALUATION: A DELICATE TRADE-OFF
▸ File size (bandwidth/storage cost)
▸ Perceptual quality compared to original
▸ Encode time (cpu cost, latency of new images)
▸ End-user experience:
▸ time-to-render
(includes latency, download, decode, progressive rendering)
▸ perceptual quality with no access to original
(e.g. blur vs blockiness, etc.)

30. Q_AUTO - EVALUATION
VISUAL INSPECTION

31. Q_AUTO - EVALUATION
VISUAL INSPECTION

32. Q_AUTO - EVALUATION
VISUAL INSPECTION

33. Q_AUTO - EVALUATION
VISUAL INSPECTION

34. Q_AUTO - EVALUATION
VISUAL INSPECTION

35. Q_AUTO - EVALUATION
VISUAL INSPECTION

36. Q_AUTO - EVALUATION
PERCEPTUAL METRICS
▸ Commonly used simple metrics are not really good
enough (e.g. RGB-PSNR)
▸ Perceptually uniform color space:
RGB < YCbCr < HSL < L*a*b*
▸ Kornel Lesiński’s DSSIM: L*a*b* multi-scale SSIM
▸ Our own metric: a variant of L*a*b* multi-scale SSIM with
some improvements

37. Q_AUTO - EVALUATION - PHOTOGRAPHIC IMAGES
CLOUDINARY_DSSIM
(MOST ACCURATE)
Y-DSSIM
(USED IN Q_AUTO)
KORNEL’S DSSIM
ARTIFACT DANGER ZONE

38. Q_AUTO - EVALUATION - NON-PHOTOGRAPHIC IMAGES

39. Q_AUTO,F_AUTO - CONCLUSION
CONCLUSION
▸ Using q_auto,f_auto ensures that every image gets
encoded at the right quality, in the best available format
▸ No visible compression artifacts
▸ No wasted bandwidth
▸ Less bandwidth needed on average
(though not for every individual image: the q_auto quality
might be higher than the default quality)

40. UPCOMING CLOUDINARY WEBINARS
Responsive Images: Past, Present & Future
Wednesday Nov 16th , 11am PT
Image Management: Buy vs. Build, Build.com
Wednesday Dec 7th , 11am PT
Don’t fall behind. Check upcoming webinars:
http://info.cloudinary.com/webinars.html

41. QUESTIONS?
q_auto,f_auto
Uncompressed
Image
NON-PHOTOGRAPHIC?
Photographic Non-photographic
CHROMA SUBSAMPLING?
TWO TRIAL JPEG ENCODINGS
(4:4:4 AND 4:2:0)
Y-DSSIM SCORE
Target quality
TRIAL LOSSLESS
PNG/WEBP
LIBJPEG
4:2:0
PNG ENCODING
(PNGQUANT)
LOSSLESS
WEBP
LOSSY
WEBP
LIBJPEG
4:4:4
MOZJPEG
4:2:0
jon@cloudinary.com