Towards Coding for Human and Machine Vision: A Scalable ...

Towards Coding for Human and Machine Vision:

A Scalable Image Coding ApproachYueyu Hu, Shuai Yang, Wenhan Yang, Ling-Yu Duan, Jiaying Liu

Peking University

IEEE ICME 2020

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SCENE

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WHAT HUMANS SEE

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MACHINE FEATURES

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MACHINE ANALYTICS

human human human human

human human human

human

human

human human human human human

human human

human human human human human human human human human



car car

flag

flag

traffic light traffic light

hair

face

up

r-arm

l-arm

r-leg

l-leg

r-shoe

l-shoe

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IMAGE CODING FOR WHOM?

HUMAN VISION

AMOUNT OF INFORMATION

1) Feature Extraction• DegradedHigh-Quality • Enhanced Information

2) Guided Reconstruction• Feature Image• Information Generation

MACHINE VISION

1) Feature Extraction• Redundant Compact• Compressed Information

2) Regression• Feature Label• Further Compressed

?HIGH-LEVEL FEATURESFOR MACHINE VISION

RECONSTRUCTED IMAGESFOR HUMAN VISION

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IMAGE CODING NEXTGEN

Ling-Yu Duan, Jiaying Liu, Wenhan Yang, Tiejun Huang, Wen Gao. Video Coding for Machines: A Paradigm of Collaborative Compression and Intelligent Analytics. arXiv:2001.03569, 2020

Video Acquisition

VCM EncoderBitstream

Bitstream

VCM Decoder

Machine Vision

Human Vision

• Scalable (according to utilizations)• Efficient compression for joint human and machine vision

Reconstructed Video

Reconstructed FeaturesVideo Stream

Feature Stream

Model Stream

ScalableFeedback

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INFORMATION DENSITY SPECTRUM

High Efficiency Video Coding

SIFT

CDVS

CDVA

Compressive Sensing

Super-Resolution

AVS3

AVS2

Versatile Video Coding

HIGH-LEVEL FEATURESFOR MACHINE VISION

RECONSTRUCTED IMAGESFOR HUMAN VISION

AMOUNT OF INFORMATION

• Descriptor coding for efficient machine vision analytics (low bit-rate)• Sophisticated video codecs for improved human vision (high bit-rate)

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EDGES• Efficient for structural information

• Maintain scalability

• Sparse and light-weight

• Supports smooth scaling

PROS

• Inefficient for details in images

• Ambiguous in color

CONS

IMAGE REPRESENTATIONS

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• Avoid color ambiguity

• Sparse and compact

• Related to visual fidelity

PROS

• Usually randomly distributed

• Inefficient for further compress

CONS

COLOR

IMAGE REPRESENTATIONS

DARK BLUE

LIGHT BLUE BLUE

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HUMAN FACES

Faces are naturally salient area in images we are looking at.

Machine vision systems to analysis faces have been widely developed.

It is the reflection of humanity in technology.

Analytics of Faces

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Input image

Decoded image

Edge extraction Structure code

Color code EncoderBitstream

Bitstream

G

Decoder

Machine Vision

Human Vision

• Conceptual compression to achieve high quality with compact features• Scalable bit-stream for different tasks• Vectorized Edges + Sparse Pixels

SCALABLE FRAMEWORK

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Input image

Decoded image



Bitstream

G

Decoder

Machine Vision

Human Vision

ENCODER · EDGE• Edge detection via structured forests

P. Dollar and C. L. Zitnick. Structured forests for fast edge detection. ICCV, 2013.

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Input image

Decoded image



Bitstream

G

Decoder

Machine Vision

Human Vision

ENCODER · EDGE• Edge detection via structured forests

• AutoTrace to convert edge pixels to vectorized representations

• Represented by lines and curves• Short and trivial edges are screened

• Prediction for Partial Matching (PPM)to losslessly compress vectors

M.Weber. AutoTrace: a program for converting bitmap to vector graphic. 1998. http://autotrace.sourceforge.net/

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Input image

Decoded image



Bitstream

G

Decoder

Machine Vision

Human Vision

ENCODER · COLOR• Sparse pixels sampled according to edges

• Segments: sample on both sides

α

αp1 p2

p1

p2

ps

ps

pt

pt

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Input image

Decoded image



Bitstream

G

Decoder

Machine Vision

Human Vision

pa

ps pt

pb

p1

ENCODER · COLOR• Sparse pixels sampled according to edges

• Segments: sample on both sizes• Curves: sample on areas with steepest

gradients

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Input image

Decoded image



Bitstream

G

Decoder

Machine Vision

Human Vision

DECODER· MACHINE VISION• Image-to-image translation

• Render pixels with vectorized representations• Edge-to-RGB translation

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Input image

Decoded image



Bitstream

G

Decoder

Machine Vision

Human Vision

DECODER· HUMAN VISION• Image-to-image translation

• Render pixels with vectorized representations• Generate masks for completion synthesis• Image inpainting

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Input image

Decoded image



Bitstream

G

Decoder

Machine Vision

Human Vision

LOSS FUNCTIONS• Reconstruction Loss

• Perceptual Loss1 2|| || SSIM( , )][r G GI I I Iλ λ= − +

3PERC( , )][p GI Iλ=

D

Reconstruction Loss + Perceptual Loss

Adversarial Loss

• Adversarial Objective

]

, , )],

[ ([ReLU( ([ReLU( ( ,

, ))] , ))

G G

D G

DM

ID ID I E M

E MEτ

τ −

= −=+

+

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EXPERIMENTAL RESULTS

HUMAN VISION

Subjective preference survey.

Measuring fidelity and Aesthetics.

MACHINE VISION

Evaluate facial landmark detection.

Measuring information preservation.

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HUMAN VISION

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SCALABLE OUTPUT

MACHINE VISION INPUT IMAGE

0.114 bpp 0.172 bpp

0.157 bpp 0.249 bpp

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HUMAN PERCEPTION

Input image JPEG 0.266 bpp

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HUMAN PERCEPTION

Input image Ours 0.249 bpp

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HUMAN PERCEPTION


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HUMAN PERCEPTION


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HUMAN PERCEPTION


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HUMAN PERCEPTION


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MACHINE VISION

2

4

8

16

32

64

0.12 0.135 0.15 0.165 0.18 0.195 0.21 0.225 0.24 0.255 0.27

Nor

mal

ized

Poi

nt-t

o-Po

int

Erro

r

Bit-Rate (bpp)

JPEG (qp=8)

JPEG (qp=8)

LANDMARK DETECTION ACCURACY

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MACHINE VISION

2

4

8

16

32

64

0.12 0.135 0.15 0.165 0.18 0.195 0.21 0.225 0.24 0.255 0.27

Nor

mal

ized

Poi

nt-t

o-Po

int

Erro

r

Bit-Rate (bpp)

JPEG (qp=8)

our (E+C)

OUR (edge+color)


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MACHINE VISION

2

4

8

16

32

64

0.12 0.135 0.15 0.165 0.18 0.195 0.21 0.225 0.24 0.255 0.27

Nor

mal

ized

Poi

nt-t

o-Po

int

Erro

r

Bit-Rate (bpp)

JPEG (qp=7)

JPEG (qp=8)

our (E+C)

JPEG (qp=7)


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4

8

16

32

64

0.12 0.135 0.15 0.165 0.18 0.195 0.21 0.225 0.24 0.255 0.27

Nor

mal

ized

Poi

nt-t

o-Po

int

Erro

r

Bit-Rate (bpp)

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MACHINE VISION

JPEG (qp=6)

JPEG (qp=7)

JPEG (qp=8)

our (E+C)

JPEG (qp=6)


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MACHINE VISION

2

4

8

16

32

64

0.12 0.135 0.15 0.165 0.18 0.195 0.21 0.225 0.24 0.255 0.27

Nor

mal

ized

Poi

nt-t

o-Po

int

Erro

r

Bit-Rate (bpp)

JPEG (qp=4)

JPEG (qp=6)

JPEG (qp=7)

JPEG (qp=8)

our (E+C)

JPEG (qp=4)


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MACHINE VISION

2

4

8

16

32

64

0.12 0.135 0.15 0.165 0.18 0.195 0.21 0.225 0.24 0.255 0.27

Nor

mal

ized

Poi

nt-t

o-Po

int

Erro

r

Bit-Rate (bpp)

JPEG (qp=4)

JPEG (qp=6)

JPEG (qp=7)

JPEG (qp=8)

our (E+C)our (E)

OUR (edge)


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MACHINE VISIONLANDMARK DETECTION ACCURACY

• Quantitatively evaluate the accuracy

of facial landmark detection on the

reconstructed images.

• Results show statistically improved

accuracy at a lower bit-rate.

• While the basic layer maintain a high

accuracy, the enhancing layer provide

more fidelity.

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MACHINE VISIONLANDMARK DETECTION RESULTS

JPEG 0.131 bpp Ours 0.115 bpp

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CONCLUSION

APPROACH TO COLLABORATIVE CODING• Edge + sparse pixels, vectorized representation• Generative adversarial reconstruction

• Human-machine collaborative feature extraction

INFORMATION SCALABLE FRAMEWORK• Base layer Semantically accurate

• Enhanced layer Visually faithful• Efficient feature adaptation

FUTURE DIRECTIONS• Self-learned feature adaptation• Multi-task collaborative inference

• Theoretical analysis on collaborative coding

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PROJECT

Towards Coding for Human and Machine Vision: A Scalable Image Coding Approach

PAPER ID 818

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Towards Coding for Human and Machine Vision: A Scalable ...

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