Deep Generative Video Compression - Stephan Mandt · 2019. 10. 27. · Deep Generative Video...

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Deep Generative Video Compression Jun Han *1 , Salvator Lombardo *1 , Christopher Schroers 1 and Stephan Mandt 2 1: Disney Research 2: UC Irvine *: shared first authorship Video Codecs & Motivation I Traditional Codecs: H.264/H265; VP9. I Pipelines 1. Motion v t from x t and ˆ x t -1 2. Predict ¯ x t and residual r t = x t - ¯ x t 3. Transform and quantize r t to ˆ y t 4. Inverse ˆ y t to residual ˆ r t 5. Entropy coding v t and ˆ y t 6. Reconstruct ˆ x t x t r t I Limitations: 1. Simple motion estimation; simple transform 2. Blocky artifacts in low-bit rate regime Codecs Comparison H.265 (0.86 bpp) t=1 t=6 VP9 (0.57 bpp) t=1 t=6 Ours (0.06 bpp) t=1 t=6 bpp: bit per pixel; original: 8 × 3 = 24 bits per pixel. I Ours: much lower bit rate; no blocky artifact. Deep Image Compression (Balle, ICLR 2017) Quantization Encoder AE Binary AD Decoder Noise Round Training After training Input Image Reconstructed Image μ φ (x) e z e z z x e x μ ( e z ) p ( e z ) Figure: e z : adding noise (training) or rounding (after training). Prior p θ ( e z ): parametric form to fit data distribution for entropy coding. I Inference model: ˜ z q φ (z | x )= U ( ˆ z - 1 2 , ˆ z + 1 2 ) , encoder ˆ z = μ φ (x ). I Loss: L(φ; θ )= E ˜ z q [log p θ (x | ˜ z )] | {z } distortion - β (0 - E ˜ z q [log p θ z )]) | {z } cross entropy: bit rate , where β adjusts rate-distortion ratio. I Outperforms best traditional image codec. Proposed Baseline Model μ φ (x t ) z t Quantization ˜ z t Encoder AE Binary AD ˜ z t Decoder ˜ x t Noise Round Training After training p z t | ˜ z <t ) x t Input Video Reconstructed Video μ ( e z t ) Figure: Two choices of predictive models for entropy coding: p θ ( e z t | e z t -1 ) Kalman Filter; p θ ( e z t | e z <t ) LSTM. I Generative model: p (x 1:T , z 1:T )= Q T t =1 p θ (z t |z <t )p θ (x t |z t ). I Encode/decode frame x t using image encoder/decoder I Leverage predictive models to capture temporal redundancy Improved Video Compression Model x 1:T x t μ φ (x 1:T ) μ φ (x t ) f z t Quantization ˜ z t Encoders AE AE Binary AD AD ˜ z t Decoder ˜ x t Noise Round Training After training p z t | ˜ z <t ) ˜ f p ( ˜ f ) μ z t , ˜ f ) ˜ f Noise Round Input Video Reconstructed Video Figure: Global state f : per-segment, inferred from a segment T of video by LSTM after encoder μ φ ; local state z t : per-frame, inferred from x t after encoder μ φ . I Split latent states of a video segment into global state (coding once) and local states (predictive model for coding) I Loss: distortion + cross-entropy E e f , e z 1:T q [log p θ (x 1:T | e f , e z 1:T )] + H [q φ ( e f |x 1:T ), p θ ( e f )] + H [q φ ( e z 1:T |x 1:T ), p θ ( e z 1:T )]. I Entropy Coding: arithmetic coding by learned prior models Choice of Probability Models I Generative Model: p θ (x 1:T , z 1:T , f )= p θ (f )p θ (z 1:T ) T Y t =1 p θ (x t |f , z 1:T ) I Encoder Model: q φ (z 1:T , f | x 1:T )= q φ (f | x 1:T ) T Y t =1 q φ (z t | x t ). ˜ f q φ (f | x 1:T )= U ( ˆ f - 1 2 , ˆ f + 1 2 ) ˜ z t q φ (z t | x t )= U ( ˆ z t - 1 2 , ˆ z t + 1 2 ) . where encoders ˆ f = μ φ (x 1:T ) and ˆ z t = μ φ (x t ). I Prior Model: p θ (f )= dim(f ) Y i p θ (f i ) *U (- 1 2 , 1 2 ); p θ (z 1:T )= T Y t dim(z ) Y i p θ (z i t | z i <t ) *U (- 1 2 , 1 2 ), Gaussian convoluted with uniform; closed-form evaluation. Verification Experiments Datasets: Sprites; BAIR; Kinetics. Latent Variable Distribution Visualization (a) p θ(f i ) (b) p θ(f j ) (c) p θ(z i 1 ) (d) p θ(z j 1 ) Figure: Empirical distributions of inference posteriors and learned prior model. Latent Variable Entropy Visualization (a) Sprites (b) BAIR (c) Kinetics Figure: Average bits of information stored in f and z 1:T Summary: global state stores much more information than local state; predictive model save bits; LSTM works better than Kalman filter. Comparison Experiments Quantitative Comparison (Rate-distortion curves) (a) Sprites (b) BAIR (c) Kinetics Qualitative Comparison Ours (0.29 bpp, 38.1 dB) Original Ours (0.39 bpp) VP9 (0.39 bpp) VP9 (0.44 bpp, 25.7 dB) 32.0 dB 29.3 dB t=1 t=5 t=10 30.1 dB 30.8 dB Figure: Compressed videos by our LSTMP-LG and VP9 in low-bit rate regime.

Transcript of Deep Generative Video Compression - Stephan Mandt · 2019. 10. 27. · Deep Generative Video...

Page 1: Deep Generative Video Compression - Stephan Mandt · 2019. 10. 27. · Deep Generative Video Compression Jun Han*1, Salvator Lombardo*1, Christopher Schroers1 and Stephan Mandt2 1:

Deep Generative Video CompressionJun Han*1 , Salvator Lombardo*1, Christopher Schroers1 and Stephan Mandt2

1: Disney Research 2: UC Irvine*: shared first authorship

Video Codecs & Motivation

I Traditional Codecs: H.264/H265; VP9.

I Pipelines1. Motion vt from xt and xt−12. Predict xt and residual rt = xt − xt3. Transform and quantize rt to yt

4. Inverse yt to residual rt5. Entropy coding vt and yt6. Reconstruct xt = xt + rt

I Limitations:1. Simple motion estimation; simple transform2. Blocky artifacts in low-bit rate regime

Codecs Comparison

H.265 (0.86 bpp)

t=1 t=6

VP9 (0.57 bpp)

t=1 t=6

Ours (0.06 bpp)

t=1 t=6

bpp: bit per pixel; original: 8× 3 = 24 bits per pixel.

I Ours: much lower bit rate; no blocky artifact.

Deep Image Compression (Balle, ICLR 2017)

QuantizationEncoder

AE

Binary

AD

Decoder

NoiseRound

Training

After training

Input Image

Reconstructed Image

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ex

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Figure: z : adding noise (training) or rounding (after training). Prior pθ(z): parametric form to fitdata distribution for entropy coding.

I Inference model: z ∼ qφ(z | x) = U(z − 1

2, z + 12

), encoder z = µφ(x).

I Loss: L(φ;θ) = Ez∼q[log pθ(x |z)]︸ ︷︷ ︸distortion

− β ( 0− Ez∼q[log pθ(z)])︸ ︷︷ ︸cross entropy: bit rate

,

where β adjusts rate-distortion ratio.

I Outperforms best traditional image codec.

Proposed Baseline Model

µ�(xt)<latexit sha1_base64="c/cNSDYktElGk5W1YWlZe5sy2FM=">AAACIHicbVDLSsNAFJ34rPUVdelmsAh1UxIR6rLoxmUF+4AmhMlk0g6dTMLMRCwhn+LGX3HjQhHd6dc4abPowwPDHM65l3vv8RNGpbKsH2NtfWNza7uyU93d2z84NI+OuzJOBSYdHLNY9H0kCaOcdBRVjPQTQVDkM9Lzx7eF33skQtKYP6hJQtwIDTkNKUZKS57ZdPyYBXIS6S9zojT3sgUlGdE8h/V57Sn31IVn1qyGNQVcJXZJaqBE2zO/nSDGaUS4wgxJObCtRLkZEopiRvKqk0qSIDxGQzLQlKOISDebHpjDc60EMIyFflzBqTrfkaFIFtvpygipkVz2CvE/b5Cq8NrNKE9SRTieDQpTBlUMi7RgQAXBik00QVhQvSvEIyQQVjrTqg7BXj55lXQvG7bVsO+vaq2bMo4KOAVnoA5s0AQtcAfaoAMweAav4B18GC/Gm/FpfM1K14yy5wQswPj9Axv9pWA=</latexit><latexit sha1_base64="c/cNSDYktElGk5W1YWlZe5sy2FM=">AAACIHicbVDLSsNAFJ34rPUVdelmsAh1UxIR6rLoxmUF+4AmhMlk0g6dTMLMRCwhn+LGX3HjQhHd6dc4abPowwPDHM65l3vv8RNGpbKsH2NtfWNza7uyU93d2z84NI+OuzJOBSYdHLNY9H0kCaOcdBRVjPQTQVDkM9Lzx7eF33skQtKYP6hJQtwIDTkNKUZKS57ZdPyYBXIS6S9zojT3sgUlGdE8h/V57Sn31IVn1qyGNQVcJXZJaqBE2zO/nSDGaUS4wgxJObCtRLkZEopiRvKqk0qSIDxGQzLQlKOISDebHpjDc60EMIyFflzBqTrfkaFIFtvpygipkVz2CvE/b5Cq8NrNKE9SRTieDQpTBlUMi7RgQAXBik00QVhQvSvEIyQQVjrTqg7BXj55lXQvG7bVsO+vaq2bMo4KOAVnoA5s0AQtcAfaoAMweAav4B18GC/Gm/FpfM1K14yy5wQswPj9Axv9pWA=</latexit><latexit sha1_base64="c/cNSDYktElGk5W1YWlZe5sy2FM=">AAACIHicbVDLSsNAFJ34rPUVdelmsAh1UxIR6rLoxmUF+4AmhMlk0g6dTMLMRCwhn+LGX3HjQhHd6dc4abPowwPDHM65l3vv8RNGpbKsH2NtfWNza7uyU93d2z84NI+OuzJOBSYdHLNY9H0kCaOcdBRVjPQTQVDkM9Lzx7eF33skQtKYP6hJQtwIDTkNKUZKS57ZdPyYBXIS6S9zojT3sgUlGdE8h/V57Sn31IVn1qyGNQVcJXZJaqBE2zO/nSDGaUS4wgxJObCtRLkZEopiRvKqk0qSIDxGQzLQlKOISDebHpjDc60EMIyFflzBqTrfkaFIFtvpygipkVz2CvE/b5Cq8NrNKE9SRTieDQpTBlUMi7RgQAXBik00QVhQvSvEIyQQVjrTqg7BXj55lXQvG7bVsO+vaq2bMo4KOAVnoA5s0AQtcAfaoAMweAav4B18GC/Gm/FpfM1K14yy5wQswPj9Axv9pWA=</latexit><latexit sha1_base64="c/cNSDYktElGk5W1YWlZe5sy2FM=">AAACIHicbVDLSsNAFJ34rPUVdelmsAh1UxIR6rLoxmUF+4AmhMlk0g6dTMLMRCwhn+LGX3HjQhHd6dc4abPowwPDHM65l3vv8RNGpbKsH2NtfWNza7uyU93d2z84NI+OuzJOBSYdHLNY9H0kCaOcdBRVjPQTQVDkM9Lzx7eF33skQtKYP6hJQtwIDTkNKUZKS57ZdPyYBXIS6S9zojT3sgUlGdE8h/V57Sn31IVn1qyGNQVcJXZJaqBE2zO/nSDGaUS4wgxJObCtRLkZEopiRvKqk0qSIDxGQzLQlKOISDebHpjDc60EMIyFflzBqTrfkaFIFtvpygipkVz2CvE/b5Cq8NrNKE9SRTieDQpTBlUMi7RgQAXBik00QVhQvSvEIyQQVjrTqg7BXj55lXQvG7bVsO+vaq2bMo4KOAVnoA5s0AQtcAfaoAMweAav4B18GC/Gm/FpfM1K14yy5wQswPj9Axv9pWA=</latexit>

zt<latexit sha1_base64="2+byp70tWvsxbofl6uQHE4ovRBw=">AAAB+XicbVDLSgMxFL3js9bXqEs3wSK4KjMi6LLoxmUF+4B2GDKZtA3NJEOSKdShf+LGhSJu/RN3/o2ZdhbaeiDkcM695OREKWfaeN63s7a+sbm1Xdmp7u7tHxy6R8dtLTNFaItILlU3wppyJmjLMMNpN1UUJxGnnWh8V/idCVWaSfFopikNEjwUbMAINlYKXbcfSR7raWKv/GkWmtCteXVvDrRK/JLUoEQzdL/6sSRZQoUhHGvd873UBDlWhhFOZ9V+pmmKyRgPac9SgROqg3yefIbOrRKjgVT2CIPm6u+NHCe6CGcnE2xGetkrxP+8XmYGN0HORJoZKsjioUHGkZGoqAHFTFFi+NQSTBSzWREZYYWJsWVVbQn+8pdXSfuy7nt1/+Gq1rgt66jAKZzBBfhwDQ24hya0gMAEnuEV3pzceXHenY/F6JpT7pzAHzifP2R7lCM=</latexit><latexit sha1_base64="2+byp70tWvsxbofl6uQHE4ovRBw=">AAAB+XicbVDLSgMxFL3js9bXqEs3wSK4KjMi6LLoxmUF+4B2GDKZtA3NJEOSKdShf+LGhSJu/RN3/o2ZdhbaeiDkcM695OREKWfaeN63s7a+sbm1Xdmp7u7tHxy6R8dtLTNFaItILlU3wppyJmjLMMNpN1UUJxGnnWh8V/idCVWaSfFopikNEjwUbMAINlYKXbcfSR7raWKv/GkWmtCteXVvDrRK/JLUoEQzdL/6sSRZQoUhHGvd873UBDlWhhFOZ9V+pmmKyRgPac9SgROqg3yefIbOrRKjgVT2CIPm6u+NHCe6CGcnE2xGetkrxP+8XmYGN0HORJoZKsjioUHGkZGoqAHFTFFi+NQSTBSzWREZYYWJsWVVbQn+8pdXSfuy7nt1/+Gq1rgt66jAKZzBBfhwDQ24hya0gMAEnuEV3pzceXHenY/F6JpT7pzAHzifP2R7lCM=</latexit><latexit sha1_base64="2+byp70tWvsxbofl6uQHE4ovRBw=">AAAB+XicbVDLSgMxFL3js9bXqEs3wSK4KjMi6LLoxmUF+4B2GDKZtA3NJEOSKdShf+LGhSJu/RN3/o2ZdhbaeiDkcM695OREKWfaeN63s7a+sbm1Xdmp7u7tHxy6R8dtLTNFaItILlU3wppyJmjLMMNpN1UUJxGnnWh8V/idCVWaSfFopikNEjwUbMAINlYKXbcfSR7raWKv/GkWmtCteXVvDrRK/JLUoEQzdL/6sSRZQoUhHGvd873UBDlWhhFOZ9V+pmmKyRgPac9SgROqg3yefIbOrRKjgVT2CIPm6u+NHCe6CGcnE2xGetkrxP+8XmYGN0HORJoZKsjioUHGkZGoqAHFTFFi+NQSTBSzWREZYYWJsWVVbQn+8pdXSfuy7nt1/+Gq1rgt66jAKZzBBfhwDQ24hya0gMAEnuEV3pzceXHenY/F6JpT7pzAHzifP2R7lCM=</latexit><latexit sha1_base64="2+byp70tWvsxbofl6uQHE4ovRBw=">AAAB+XicbVDLSgMxFL3js9bXqEs3wSK4KjMi6LLoxmUF+4B2GDKZtA3NJEOSKdShf+LGhSJu/RN3/o2ZdhbaeiDkcM695OREKWfaeN63s7a+sbm1Xdmp7u7tHxy6R8dtLTNFaItILlU3wppyJmjLMMNpN1UUJxGnnWh8V/idCVWaSfFopikNEjwUbMAINlYKXbcfSR7raWKv/GkWmtCteXVvDrRK/JLUoEQzdL/6sSRZQoUhHGvd873UBDlWhhFOZ9V+pmmKyRgPac9SgROqg3yefIbOrRKjgVT2CIPm6u+NHCe6CGcnE2xGetkrxP+8XmYGN0HORJoZKsjioUHGkZGoqAHFTFFi+NQSTBSzWREZYYWJsWVVbQn+8pdXSfuy7nt1/+Gq1rgt66jAKZzBBfhwDQ24hya0gMAEnuEV3pzceXHenY/F6JpT7pzAHzifP2R7lCM=</latexit>

Quantization

zt<latexit sha1_base64="Yt2K9k0MRgUKaCEUNNmLvHQQcUM=">AAACAXicbVBNS8NAEN3Ur1q/ol4EL8EieCqJCHosevFYwbZCE8Jms2mXbjZhdyLUEC/+FS8eFPHqv/Dmv3HT5qCtD5Z9vDfDzLwg5UyBbX8btaXlldW1+npjY3Nre8fc3eupJJOEdknCE3kXYEU5E7QLDDi9SyXFccBpPxhflX7/nkrFEnELk5R6MR4KFjGCQUu+eeAC4yHN3SDhoZrE+ssfisIH32zaLXsKa5E4FWmiCh3f/HLDhGQxFUA4Vmrg2Cl4OZbACKdFw80UTTEZ4yEdaCpwTJWXTy8orGOthFaUSP0EWFP1d0eOY1VupytjDCM175Xif94gg+jCy5lIM6CCzAZFGbcgsco4rJBJSoBPNMFEMr2rRUZYYgI6tIYOwZk/eZH0TluO3XJuzprtyyqOOjpER+gEOegctdE16qAuIugRPaNX9GY8GS/Gu/ExK60ZVc8++gPj8wf1ppfZ</latexit><latexit sha1_base64="Yt2K9k0MRgUKaCEUNNmLvHQQcUM=">AAACAXicbVBNS8NAEN3Ur1q/ol4EL8EieCqJCHosevFYwbZCE8Jms2mXbjZhdyLUEC/+FS8eFPHqv/Dmv3HT5qCtD5Z9vDfDzLwg5UyBbX8btaXlldW1+npjY3Nre8fc3eupJJOEdknCE3kXYEU5E7QLDDi9SyXFccBpPxhflX7/nkrFEnELk5R6MR4KFjGCQUu+eeAC4yHN3SDhoZrE+ssfisIH32zaLXsKa5E4FWmiCh3f/HLDhGQxFUA4Vmrg2Cl4OZbACKdFw80UTTEZ4yEdaCpwTJWXTy8orGOthFaUSP0EWFP1d0eOY1VupytjDCM175Xif94gg+jCy5lIM6CCzAZFGbcgsco4rJBJSoBPNMFEMr2rRUZYYgI6tIYOwZk/eZH0TluO3XJuzprtyyqOOjpER+gEOegctdE16qAuIugRPaNX9GY8GS/Gu/ExK60ZVc8++gPj8wf1ppfZ</latexit><latexit sha1_base64="Yt2K9k0MRgUKaCEUNNmLvHQQcUM=">AAACAXicbVBNS8NAEN3Ur1q/ol4EL8EieCqJCHosevFYwbZCE8Jms2mXbjZhdyLUEC/+FS8eFPHqv/Dmv3HT5qCtD5Z9vDfDzLwg5UyBbX8btaXlldW1+npjY3Nre8fc3eupJJOEdknCE3kXYEU5E7QLDDi9SyXFccBpPxhflX7/nkrFEnELk5R6MR4KFjGCQUu+eeAC4yHN3SDhoZrE+ssfisIH32zaLXsKa5E4FWmiCh3f/HLDhGQxFUA4Vmrg2Cl4OZbACKdFw80UTTEZ4yEdaCpwTJWXTy8orGOthFaUSP0EWFP1d0eOY1VupytjDCM175Xif94gg+jCy5lIM6CCzAZFGbcgsco4rJBJSoBPNMFEMr2rRUZYYgI6tIYOwZk/eZH0TluO3XJuzprtyyqOOjpER+gEOegctdE16qAuIugRPaNX9GY8GS/Gu/ExK60ZVc8++gPj8wf1ppfZ</latexit><latexit sha1_base64="Yt2K9k0MRgUKaCEUNNmLvHQQcUM=">AAACAXicbVBNS8NAEN3Ur1q/ol4EL8EieCqJCHosevFYwbZCE8Jms2mXbjZhdyLUEC/+FS8eFPHqv/Dmv3HT5qCtD5Z9vDfDzLwg5UyBbX8btaXlldW1+npjY3Nre8fc3eupJJOEdknCE3kXYEU5E7QLDDi9SyXFccBpPxhflX7/nkrFEnELk5R6MR4KFjGCQUu+eeAC4yHN3SDhoZrE+ssfisIH32zaLXsKa5E4FWmiCh3f/HLDhGQxFUA4Vmrg2Cl4OZbACKdFw80UTTEZ4yEdaCpwTJWXTy8orGOthFaUSP0EWFP1d0eOY1VupytjDCM175Xif94gg+jCy5lIM6CCzAZFGbcgsco4rJBJSoBPNMFEMr2rRUZYYgI6tIYOwZk/eZH0TluO3XJuzprtyyqOOjpER+gEOegctdE16qAuIugRPaNX9GY8GS/Gu/ExK60ZVc8++gPj8wf1ppfZ</latexit>

Encoder

AE

Binary

ADzt

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Decoder

xt<latexit sha1_base64="X4Q1aFm5XrHWDo+IKYEVuO42eN8=">AAACAXicbVBNS8NAEN3Ur1q/ol4EL8EieCqJCHosevFYwbZCE8Jms2mXbjZhdyKWEC/+FS8eFPHqv/Dmv3HT5qCtD5Z9vDfDzLwg5UyBbX8btaXlldW1+npjY3Nre8fc3eupJJOEdknCE3kXYEU5E7QLDDi9SyXFccBpPxhflX7/nkrFEnELk5R6MR4KFjGCQUu+eeAC4yHN3SDhoZrE+ssfisIH32zaLXsKa5E4FWmiCh3f/HLDhGQxFUA4Vmrg2Cl4OZbACKdFw80UTTEZ4yEdaCpwTJWXTy8orGOthFaUSP0EWFP1d0eOY1VupytjDCM175Xif94gg+jCy5lIM6CCzAZFGbcgsco4rJBJSoBPNMFEMr2rRUZYYgI6tIYOwZk/eZH0TluO3XJuzprtyyqOOjpER+gEOegctdE16qAuIugRPaNX9GY8GS/Gu/ExK60ZVc8++gPj8wfylpfX</latexit><latexit sha1_base64="X4Q1aFm5XrHWDo+IKYEVuO42eN8=">AAACAXicbVBNS8NAEN3Ur1q/ol4EL8EieCqJCHosevFYwbZCE8Jms2mXbjZhdyKWEC/+FS8eFPHqv/Dmv3HT5qCtD5Z9vDfDzLwg5UyBbX8btaXlldW1+npjY3Nre8fc3eupJJOEdknCE3kXYEU5E7QLDDi9SyXFccBpPxhflX7/nkrFEnELk5R6MR4KFjGCQUu+eeAC4yHN3SDhoZrE+ssfisIH32zaLXsKa5E4FWmiCh3f/HLDhGQxFUA4Vmrg2Cl4OZbACKdFw80UTTEZ4yEdaCpwTJWXTy8orGOthFaUSP0EWFP1d0eOY1VupytjDCM175Xif94gg+jCy5lIM6CCzAZFGbcgsco4rJBJSoBPNMFEMr2rRUZYYgI6tIYOwZk/eZH0TluO3XJuzprtyyqOOjpER+gEOegctdE16qAuIugRPaNX9GY8GS/Gu/ExK60ZVc8++gPj8wfylpfX</latexit><latexit sha1_base64="X4Q1aFm5XrHWDo+IKYEVuO42eN8=">AAACAXicbVBNS8NAEN3Ur1q/ol4EL8EieCqJCHosevFYwbZCE8Jms2mXbjZhdyKWEC/+FS8eFPHqv/Dmv3HT5qCtD5Z9vDfDzLwg5UyBbX8btaXlldW1+npjY3Nre8fc3eupJJOEdknCE3kXYEU5E7QLDDi9SyXFccBpPxhflX7/nkrFEnELk5R6MR4KFjGCQUu+eeAC4yHN3SDhoZrE+ssfisIH32zaLXsKa5E4FWmiCh3f/HLDhGQxFUA4Vmrg2Cl4OZbACKdFw80UTTEZ4yEdaCpwTJWXTy8orGOthFaUSP0EWFP1d0eOY1VupytjDCM175Xif94gg+jCy5lIM6CCzAZFGbcgsco4rJBJSoBPNMFEMr2rRUZYYgI6tIYOwZk/eZH0TluO3XJuzprtyyqOOjpER+gEOegctdE16qAuIugRPaNX9GY8GS/Gu/ExK60ZVc8++gPj8wfylpfX</latexit><latexit sha1_base64="X4Q1aFm5XrHWDo+IKYEVuO42eN8=">AAACAXicbVBNS8NAEN3Ur1q/ol4EL8EieCqJCHosevFYwbZCE8Jms2mXbjZhdyKWEC/+FS8eFPHqv/Dmv3HT5qCtD5Z9vDfDzLwg5UyBbX8btaXlldW1+npjY3Nre8fc3eupJJOEdknCE3kXYEU5E7QLDDi9SyXFccBpPxhflX7/nkrFEnELk5R6MR4KFjGCQUu+eeAC4yHN3SDhoZrE+ssfisIH32zaLXsKa5E4FWmiCh3f/HLDhGQxFUA4Vmrg2Cl4OZbACKdFw80UTTEZ4yEdaCpwTJWXTy8orGOthFaUSP0EWFP1d0eOY1VupytjDCM175Xif94gg+jCy5lIM6CCzAZFGbcgsco4rJBJSoBPNMFEMr2rRUZYYgI6tIYOwZk/eZH0TluO3XJuzprtyyqOOjpER+gEOegctdE16qAuIugRPaNX9GY8GS/Gu/ExK60ZVc8++gPj8wfylpfX</latexit>

NoiseRound

Training

After training

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Input Video

Reconstructed Video

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Figure: Two choices of predictive models for entropy coding: pθ(z t | z t−1)Kalman Filter; pθ(z t | z<t) LSTM.

I Generative model: p(x1:T , z1:T ) =∏T

t=1 pθ(z t|z<t)pθ(x t|z t).

I Encode/decode frame x t using image encoder/decoder

I Leverage predictive models to capture temporal redundancy

Improved Video Compression Model

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f<latexit sha1_base64="KT3gym11XAjKBtmQlxpgoE5y3+0=">AAAB9XicbVDLSgMxFL1TX7W+qi7dBIvgqsyIUJdFNy4r2Ae0Y8lkMm1oJhmSjFKG/ocbF4q49V/c+Tdm2llo64GQwzn3kpMTJJxp47rfTmltfWNzq7xd2dnd2z+oHh51tEwVoW0iuVS9AGvKmaBtwwynvURRHAecdoPJTe53H6nSTIp7M02oH+ORYBEj2FjpYRBIHuppbK8smg2rNbfuzoFWiVeQGhRoDatfg1CSNKbCEI617ntuYvwMK8MIp7PKINU0wWSCR7RvqcAx1X42Tz1DZ1YJUSSVPcKgufp7I8OxzqPZyRibsV72cvE/r5+a6MrPmEhSQwVZPBSlHBmJ8gpQyBQlhk8twUQxmxWRMVaYGFtUxZbgLX95lXQu6p5b9+4ua83roo4ynMApnIMHDWjCLbSgDQQUPMMrvDlPzovz7nwsRktOsXMMf+B8/gA38pL3</latexit><latexit sha1_base64="KT3gym11XAjKBtmQlxpgoE5y3+0=">AAAB9XicbVDLSgMxFL1TX7W+qi7dBIvgqsyIUJdFNy4r2Ae0Y8lkMm1oJhmSjFKG/ocbF4q49V/c+Tdm2llo64GQwzn3kpMTJJxp47rfTmltfWNzq7xd2dnd2z+oHh51tEwVoW0iuVS9AGvKmaBtwwynvURRHAecdoPJTe53H6nSTIp7M02oH+ORYBEj2FjpYRBIHuppbK8smg2rNbfuzoFWiVeQGhRoDatfg1CSNKbCEI617ntuYvwMK8MIp7PKINU0wWSCR7RvqcAx1X42Tz1DZ1YJUSSVPcKgufp7I8OxzqPZyRibsV72cvE/r5+a6MrPmEhSQwVZPBSlHBmJ8gpQyBQlhk8twUQxmxWRMVaYGFtUxZbgLX95lXQu6p5b9+4ua83roo4ynMApnIMHDWjCLbSgDQQUPMMrvDlPzovz7nwsRktOsXMMf+B8/gA38pL3</latexit><latexit sha1_base64="KT3gym11XAjKBtmQlxpgoE5y3+0=">AAAB9XicbVDLSgMxFL1TX7W+qi7dBIvgqsyIUJdFNy4r2Ae0Y8lkMm1oJhmSjFKG/ocbF4q49V/c+Tdm2llo64GQwzn3kpMTJJxp47rfTmltfWNzq7xd2dnd2z+oHh51tEwVoW0iuVS9AGvKmaBtwwynvURRHAecdoPJTe53H6nSTIp7M02oH+ORYBEj2FjpYRBIHuppbK8smg2rNbfuzoFWiVeQGhRoDatfg1CSNKbCEI617ntuYvwMK8MIp7PKINU0wWSCR7RvqcAx1X42Tz1DZ1YJUSSVPcKgufp7I8OxzqPZyRibsV72cvE/r5+a6MrPmEhSQwVZPBSlHBmJ8gpQyBQlhk8twUQxmxWRMVaYGFtUxZbgLX95lXQu6p5b9+4ua83roo4ynMApnIMHDWjCLbSgDQQUPMMrvDlPzovz7nwsRktOsXMMf+B8/gA38pL3</latexit><latexit sha1_base64="KT3gym11XAjKBtmQlxpgoE5y3+0=">AAAB9XicbVDLSgMxFL1TX7W+qi7dBIvgqsyIUJdFNy4r2Ae0Y8lkMm1oJhmSjFKG/ocbF4q49V/c+Tdm2llo64GQwzn3kpMTJJxp47rfTmltfWNzq7xd2dnd2z+oHh51tEwVoW0iuVS9AGvKmaBtwwynvURRHAecdoPJTe53H6nSTIp7M02oH+ORYBEj2FjpYRBIHuppbK8smg2rNbfuzoFWiVeQGhRoDatfg1CSNKbCEI617ntuYvwMK8MIp7PKINU0wWSCR7RvqcAx1X42Tz1DZ1YJUSSVPcKgufp7I8OxzqPZyRibsV72cvE/r5+a6MrPmEhSQwVZPBSlHBmJ8gpQyBQlhk8twUQxmxWRMVaYGFtUxZbgLX95lXQu6p5b9+4ua83roo4ynMApnIMHDWjCLbSgDQQUPMMrvDlPzovz7nwsRktOsXMMf+B8/gA38pL3</latexit>

zt<latexit sha1_base64="2+byp70tWvsxbofl6uQHE4ovRBw=">AAAB+XicbVDLSgMxFL3js9bXqEs3wSK4KjMi6LLoxmUF+4B2GDKZtA3NJEOSKdShf+LGhSJu/RN3/o2ZdhbaeiDkcM695OREKWfaeN63s7a+sbm1Xdmp7u7tHxy6R8dtLTNFaItILlU3wppyJmjLMMNpN1UUJxGnnWh8V/idCVWaSfFopikNEjwUbMAINlYKXbcfSR7raWKv/GkWmtCteXVvDrRK/JLUoEQzdL/6sSRZQoUhHGvd873UBDlWhhFOZ9V+pmmKyRgPac9SgROqg3yefIbOrRKjgVT2CIPm6u+NHCe6CGcnE2xGetkrxP+8XmYGN0HORJoZKsjioUHGkZGoqAHFTFFi+NQSTBSzWREZYYWJsWVVbQn+8pdXSfuy7nt1/+Gq1rgt66jAKZzBBfhwDQ24hya0gMAEnuEV3pzceXHenY/F6JpT7pzAHzifP2R7lCM=</latexit><latexit sha1_base64="2+byp70tWvsxbofl6uQHE4ovRBw=">AAAB+XicbVDLSgMxFL3js9bXqEs3wSK4KjMi6LLoxmUF+4B2GDKZtA3NJEOSKdShf+LGhSJu/RN3/o2ZdhbaeiDkcM695OREKWfaeN63s7a+sbm1Xdmp7u7tHxy6R8dtLTNFaItILlU3wppyJmjLMMNpN1UUJxGnnWh8V/idCVWaSfFopikNEjwUbMAINlYKXbcfSR7raWKv/GkWmtCteXVvDrRK/JLUoEQzdL/6sSRZQoUhHGvd873UBDlWhhFOZ9V+pmmKyRgPac9SgROqg3yefIbOrRKjgVT2CIPm6u+NHCe6CGcnE2xGetkrxP+8XmYGN0HORJoZKsjioUHGkZGoqAHFTFFi+NQSTBSzWREZYYWJsWVVbQn+8pdXSfuy7nt1/+Gq1rgt66jAKZzBBfhwDQ24hya0gMAEnuEV3pzceXHenY/F6JpT7pzAHzifP2R7lCM=</latexit><latexit sha1_base64="2+byp70tWvsxbofl6uQHE4ovRBw=">AAAB+XicbVDLSgMxFL3js9bXqEs3wSK4KjMi6LLoxmUF+4B2GDKZtA3NJEOSKdShf+LGhSJu/RN3/o2ZdhbaeiDkcM695OREKWfaeN63s7a+sbm1Xdmp7u7tHxy6R8dtLTNFaItILlU3wppyJmjLMMNpN1UUJxGnnWh8V/idCVWaSfFopikNEjwUbMAINlYKXbcfSR7raWKv/GkWmtCteXVvDrRK/JLUoEQzdL/6sSRZQoUhHGvd873UBDlWhhFOZ9V+pmmKyRgPac9SgROqg3yefIbOrRKjgVT2CIPm6u+NHCe6CGcnE2xGetkrxP+8XmYGN0HORJoZKsjioUHGkZGoqAHFTFFi+NQSTBSzWREZYYWJsWVVbQn+8pdXSfuy7nt1/+Gq1rgt66jAKZzBBfhwDQ24hya0gMAEnuEV3pzceXHenY/F6JpT7pzAHzifP2R7lCM=</latexit><latexit sha1_base64="2+byp70tWvsxbofl6uQHE4ovRBw=">AAAB+XicbVDLSgMxFL3js9bXqEs3wSK4KjMi6LLoxmUF+4B2GDKZtA3NJEOSKdShf+LGhSJu/RN3/o2ZdhbaeiDkcM695OREKWfaeN63s7a+sbm1Xdmp7u7tHxy6R8dtLTNFaItILlU3wppyJmjLMMNpN1UUJxGnnWh8V/idCVWaSfFopikNEjwUbMAINlYKXbcfSR7raWKv/GkWmtCteXVvDrRK/JLUoEQzdL/6sSRZQoUhHGvd873UBDlWhhFOZ9V+pmmKyRgPac9SgROqg3yefIbOrRKjgVT2CIPm6u+NHCe6CGcnE2xGetkrxP+8XmYGN0HORJoZKsjioUHGkZGoqAHFTFFi+NQSTBSzWREZYYWJsWVVbQn+8pdXSfuy7nt1/+Gq1rgt66jAKZzBBfhwDQ24hya0gMAEnuEV3pzceXHenY/F6JpT7pzAHzifP2R7lCM=</latexit>

Quantization

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Encoders

AE

AE

Binary

AD

AD

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Decoder

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NoiseRound

Training

After training

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NoiseRound

Input Video

Reconstructed Video

Figure: Global state f : per-segment, inferred from a segment T of video by LSTMafter encoder µφ; local state zt: per-frame, inferred from x t after encoder µφ.

I Split latent states of a video segment into global state (codingonce) and local states (predictive model for coding)

I Loss: distortion + cross-entropy

Ef ,z1:T∼q[log pθ(x1:T |f , z1:T )] + H [qφ(f |x1:T ), pθ(f )]

+ H [qφ(z1:T |x1:T ), pθ(z1:T )].

I Entropy Coding: arithmetic coding by learned prior models

Choice of Probability Models

I Generative Model:

pθ(x1:T , z1:T , f ) = pθ(f )pθ(z1:T )T∏t=1

pθ(x t|f , z1:T )

I Encoder Model:

qφ(z1:T , f | x1:T ) = qφ(f | x1:T )T∏t=1

qφ(z t | x t).

f ∼ qφ(f | x1:T ) = U(f − 1

2, f +

1

2

)z t ∼ qφ(z t | x t) = U

(z t −

1

2, z t +

1

2

).

where encoders f = µφ(x1:T ) and z t = µφ(x t).I Prior Model:

pθ(f ) =

dim(f )∏i

pθ(f i) ∗ U(−1

2,

1

2);

pθ(z1:T ) =T∏t

dim(z)∏i

pθ(z it | z i<t) ∗ U(−1

2,

1

2),

Gaussian convoluted with uniform; closed-form evaluation.

Verification Experiments

Datasets: Sprites; BAIR; Kinetics.

Latent Variable Distribution Visualization

4 2 0 2 4Observation

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Inference PosteriorPrior Distribution

(a) pθ(f i) (b) pθ(f j) (c) pθ(z i1) (d) pθ(z j1)

Figure: Empirical distributions of inference posteriors and learned prior model.

Latent Variable Entropy Visualization

(a) Sprites (b) BAIR (c) Kinetics

f z1 z2 z3 z4 z5 z6 z7 z81.5

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ntro

py [b

its]

LSTMP-LKFP-LGLSTMP-LG

f z1 z2 z3 z4 z5 z6 z7 z8 z9 z102.8

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ntro

py [b

its]

f z1 z2 z3 z4 z5 z6 z7 z8 z9 z102.8

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ntro

py [b

its]

Figure: Average bits of information stored in f and z1:T

Summary: global state stores much more information than local state;predictive model save bits; LSTM works better than Kalman filter.

Comparison Experiments

Quantitative Comparison (Rate-distortion curves)

(a) Sprites (b) BAIR (c) Kinetics

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B [d

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H.264H.265VP9KFP-LGLSTMP-LGLSTMP-L

Qualitative Comparison

Ours (0.29 bpp, 38.1 dB) Original Ours (0.39 bpp)VP9 (0.39 bpp)

VP9 (0.44 bpp, 25.7 dB) 32.0 dB 29.3 dB

t=1 t=5 t=10 30.1 dB 30.8 dB

Figure: Compressed videos by our LSTMP-LG and VP9 in low-bit rate regime.