O VERFITTING FOR F UN AND P ROFIT : I NSTANCE -A DAPTIVE D ATA C OMPRESSION
A BSTRACT
Neural data compression has been shown to outperform classical methods in terms of rate-distortion ( RD ) performance, with results still improving rapidly.
At a high level, neural compression is based on an autoencoder that tries to reconstruct the input instance from a (quantized) latent representation coupled with a prior that is used to losslessly compress these latents.
Due to limitations on model capacity and imperfect optimization and generalization, such models will subopti mally compress test data in general.
However, one of the great strengths of learned compression is that if the test time data distribution is known and relatively low entropy (e.g
a camera watching a static scene a dash cam in an autonomous car etc.
the model can easily be ﬁnetuned or adapted to this distribution leading to improved RD performance.
In this paper we take this concept to the extreme adapting the full model to a single video, and sending model updates
(quantized and compressed using a parameter space prior) along with the latent represen
- tation
Unlike previous work, we ﬁnetune not only the encoder/ latents but the entire model and - during ﬁnetuning - take into account both the effect of model quantization and the additional costs incurred by sending the model updates.
We evaluate an image compression model on I-frames (sampled at 2 fps) from videos of the Xiph dataset, and demonstrate that full-model adaptation improves RD performance by ∼ 1 dB, with respect to encoder-only ﬁnetuning.
I NTRODUCTION
The most common approach to neural lossy compression is to train a variational autoencoder (VAE)- like model on a training dataset to minimize the expected RD cost D + βR (Theis et al., 2017;
Kingma & Welling, 2013).
Although this approach has proven to be very successful (Ball´e et al., 2018), a model trained to minimize expected RD cost over a full dataset is unlikely to be optimal for every test instance because the model has limited capacity, and both optimization and generalization will be imperfect.
The problem of generalization will be especially signiﬁcant when the testing distribution is different from the training distribution, as is likely to be the case in practice.
Suboptimality of the encoder has been studied extensively under the term inference suboptimality (Cremer et al., 2018), and it has been shown that ﬁnetuning the encoder or latents for a particular instance can lead to improved compression performance (Lu et al., 2020; Campos et al., 2019; Yang et al., 2020b; Guo et al., 2020).
This approach is appealing as no additional information needs to be added to the bitstream, and nothing changes on the receiver side.
Performance gains however are limited, because the prior and decoder can not be adapted.
In this paper we present a method for full-model instance-adaptive compression , i.e. adapting the entire model to a single data instance.
Unlike previous work, our method takes into account the costs for sending not only the latent prior, but also the decoder model updates, as well as quantization of these updates.
This is achieved by extending the typical RD loss with an additional model rate term
M that measures the number of bits required to send the model updates under a newly introduced model prior , resulting in a combined RDM loss.
As an initial proof of concept, we show that this approach can lead to very substantial gains in
RD performance ( ∼ 1 dB PSNR gain at the same bitrate) on the problem of I-frame video coding, where a set of key frames, sampled from a video at 2 fps, are independently coded using an I-frame (image compression) model.
Additionally, we show how the model rate bits are distributed across the model, and (by means of an ablation study) quantify the individual gains achieved by including a model-rate loss and using quantization-aware ﬁnetuning.
The rest of this paper is structured as follows.
Section 2 discusses the basics of neural compression and related work on adaptive compression.
Section 3 presents our method, including details on the RDM loss, the choice of the model prior, its quantization, and the (de)coding procedure.
In
Sections 4 and 5 we present our experiments and results, followed by a discussion in Section 6.
P RELIMINARIES AND R ELATED W ORK 2. N EURAL DATA COMPRESSION
The standard approach to neural compression can be understood as a particular kind of VAE (Kingma & Welling, 2013).
In the compression literature the encoder q φ ( z | x ) is typically deﬁned by a neural network parameterized by φ , with either deterministic output (so q φ ( z | x ) is one-hot)
(Habibian et al., 2019) or with ﬁxed uniform [0 , 1] noise on the outputs (Ball´e et al., 2018).
In both cases, sampling z ∼ q φ ( z | x ) is used during training while quantization is used at test time.
The latent z is encoded to the bitstream using entropy coding in conjunction with a latent prior p θ ( z ) , so that coding z takes about − log p θ ( z ) bits (up to discretization).
On the receiving side, the entropy decoder is used with the same prior p θ ( z ) to decode z and then reconstruct ˆ x using the decoder network p θ ( x | z ) (note that we use the same symbol θ to denote the parameters of the prior and decoder jointly, as in our method both will have to be coded and added to the bitstream).
From these considerations it is clear that the rate R and distortion D can be measured by the two terms in the following loss:
[Equation]
This loss is equal (up to the tradeoff parameter β and an additive constant) to the standard negative evidence lower bound (ELBO) used in VAE training.
The rate term of ELBO is written as a KL divergence between encoder and prior, but since D KL ( q, p ) =
R − H [ q ] , and the encoder entropy
H [ q ] is constant in our case, minimizing the KL loss is equivalent to minimizing the rate loss.
Neural video compression is typically decomposed into the problem of independently compressing a set of key frames (i.e. I-frames) and conditionally compressing the remaining frames (Lu et al., 2019; Liu et al., 2020; Wu et al., 2018; Djelouah et al., 2019;
Yang et al., 2020a).
In this work, we speciﬁcally focus on improving I-frame compression.
2. A DAPTIVE C OMPRESSION
A compression model is trained on a dataset D with the aim of achieving optimal RD performance on test data.
However, because of limited model capacity, optimization difﬁculties, or insufﬁcient data (resulting in poor generalization), the model will in general not achieve this goal.
When the test data distribution differs from that of the training data, generalization will not be guaranteed even in the limit of inﬁnite data and model capacity, and perfect optimization.
A convenient feature of neural compression however is that a model can easily be ﬁnetuned on new data or data from a speciﬁc domain.
A model can for instance (further) be trained after deployment, and Habibian et al.
(2019) showed improved RD gains after ﬁnetuning a video compression model on footage from a dash cam, an approach dubbed adaptive compression (Habibian et al., 2019).
In adaptive compression, decoding requires access to the adapted prior and decoder models.
These models (or their delta relative to a pretrained shared model) thus need to be signaled.
When the amount of data coded with the adapted model is large, the cost of signaling the model update will be negligible as it is amortized.
However, a tradeoff exists, the more restricted the domain of adaptation, the more we can expect to gain from adaptation (e.g. an image compared to a video or collection of videos).
In this paper we consider the case where the domain of adaptation is a set of I-frames from a single video, resulting in costs for sending model updates which become very relevant.
2. C LOSING THE AMORTIZATION GAP
Coding model updates can easily become prohibitively expensive when the model is adapted for every instance.
However, if we only adapt the encoder or latents, no model update needs to be added to the bitstream, since the encoder is not needed for decoding as the latents are sent anyway.
We can thus close, or at least reduce, the amortization gap (the difference between q φ ( z | x ) and the optimal encoder;
Cremer et al.
without paying any bits for model updates.
Various authors have investigated this approach: Aytekin et al.
; Lu et al.
(2020) adapt the encoder, while Campos et al.
; Yang et al.
(2020b); Guo et al.
(2020) adapt the latents directly.
This simple approach was shown to provide a modest boost in RD performance.
2. E NCODING MODEL UPDATES
As mentioned, when adapting (parts of) the decoder or prior to an instance, model updates have to be added to the bitstream in order to enable decoding.
Recent works have proposed ways to ﬁnetune parts of the model, while keeping the resulting bitrate overhead small.
For instance Klopp et al.
(2020) train a reconstruction error predicting network at encoding time, quantize its parameters, and add them to the bitstream.
Similarly (Lam et al., 2019; 2020) propose to ﬁnetune all parameters or only the convolutional biases, respectively, of an artifact removal ﬁlter that operates after decoding.
A sparsity-enforcing and magnitude-suppressing penalty is leveraged, and additional thresholding is applied to even more strictly enforce sparsity.
The update vector is thereafter quantized using k-means clustering.
Finally, Zou et al.
(2020) ﬁnetune the latents in a meta-learning framework, in addition to updating the decoder convolutional biases, which are quantized by k-means and thereafter transmitted.
All these methods perform quantization post-training, leading to a potentially unbounded reduction in performance.
Also, albeit the use of regularizing loss terms, no valid proxy for the actual cost of sending model updates is adopted.
Finally, none of these methods performs adaptation of the full model.
The ﬁeld of model compression is related to our work as the main question to be answered is how to most efﬁciently compress a neural network without compromising on downstream task performance (Han et al., 2016; Kuzmin et al., 2019).
Bayesian compression is closely related, where the model weights are sent under a model prior (Louizos et al., 2017; Havasi et al., 2018) as is the case in our method.
Instead of modeling uncertainty in parameters, we however assume a deterministic posterior (i.e. point estimate).
Another key difference with these works is that we send the model parameter updates relative to an existing baseline model, which enables extreme compression rates (0.02-0.2 bits/param).
This concept of compressing updates has been used before in the context of federated learning (McMahan et al., 2017;
Alistarh et al., 2017) as well.
We distinguish ourselves from that context, as there the model has to be compressed during every iteration, allowing for error corrections in later iterations.
We only transmit the model updates once for every data instance that we ﬁnetune on.
F ULL -M ODEL I NSTANCE -A DAPTIVE C OMPRESSION
In this section we present full-model ﬁnetuning on one instance, while (during ﬁnetuning) taking into account both model quantization and the costs for sending model updates.
The main idea is described in Section 3.1, after which Section 3.2 and 3.3 respectively provide details regarding the model prior and its quantization.
The algorithm is described in Section 3.4.
3. F INETUNING AT INFERENCE TIME
Full-model instance-adaptive compression entails ﬁnetuning of a set of global model parameters { φ D , θ D } (obtained by training on dataset D ) on a single instance x .
This results in updated parameters φ, θ , of which only θ has to be signaled in the bitstream.
In practice we only learn the changes with respect to the global model, and encode the model updates δ = θ − θ D of the decoding model.
In order to encode δ , we introduce a continuous model prior p ( δ ) to regularize these updates, and use the quantized counterpart p [¯ δ ] for entropy (de)coding them (more on quantization in Section 3.3).
The overhead for sending quantized model update ¯ δ is given by model rate Ď
M = − [Equation] p [¯ δ ] , and can be approximated by its continuous counterpart M = − log p ( δ ) (see Appendix A.1 for justiﬁcation).
Adding this term to the standard RD loss using the same tradeoff parameter β , we obtain the instance-adaptive compression objective:
[Equation]
At inference time, this objective can be minimized directly to ﬁnd the optimal model parameters for transmitting datapoint x .
It takes into account the additional costs for encoding model updates ( M term), and incorporates model quantization during ﬁnetuning ( L RD evaluated at ¯ θ = θ D
+ ¯ δ ).
3. M ODEL PRIOR DESIGN
A plethora of options exist for designing model prior p ( δ ) as any probability density function (PDF) could be chosen, but a natural choice for modeling parameter update δ = θ − θ D is to leverage
a
Gaussian distribution, centered around the zero-update.
Speciﬁcally, we can deﬁne the model prior on the updates as a multivariate zero-centered Gaussian with zero covariance, and a single shared (hyperparameter) σ , denoting the standard deviation: p ( δ ) =
N ( δ | 0 , σ I ) .
Note that this is equivalent to modeling θ by p ( θ ) =
N ( θ | θ D , σ I ) .
When entropy (de)coding the quantized updates ¯ δ under p [¯ δ ] , we must realize that even the zeroupdate, i.e. ¯ δ = 0 , is not for free.
We deﬁne these initial static costs as
[Equation]
Because the mode of the deﬁned model prior is zero, these initial costs
[Equation]
M 0 equal the minimum costs.
Minimization of eq.
(2) thus ensures that – after overcoming these static costs – any extra bit spent on model updates will be accompanied by a commensurate improvement in RD performance.
Since our method works best when signaling the zero-update is cheap, we want to increase the probability mass p [¯ δ = 0 ] .
We propose to generalize our earlier proposed model prior to a so-called spike-and-slab prior (Roˇckov´a & George, 2018), which drastically reduces the costs for this zeroupdate.
More speciﬁcally, we redeﬁne the PDF as a (weighted) sum of two Gaussians – a wide (slab) and a more narrow (spike) distribution:
[Equation]
where α ∈ R ≥ 0 is a hyperparameter determining the height of the spiky Gaussian with respect to the wider slab, t is the the bin width used for quantization (more details in Section 3.3), and σ >> t 6 .
By choosing the standard deviation of the spike to be t 6 , the mass within six standard deviations (i.e. 99 .
7% of the total mass) is included in the central quantization bin after quantization.
Note that the (slab-only) Gaussian prior is a special case of the spike-and-slab Gaussian prior in eq.
(3), where α = 0 .
As such, p ( δ ) refers to the spike-and-slab prior in the rest of this work.
Appendix A. compares the continuous and discrete spike-and-slab prior and its gradients.
Adding the spike distribution, not only decreases Ď
M 0 , it also more heavily enforces sparsity on the updates via regularizing term M in eq.
In fact, a high spike (i.e. large α ) can make the bits for signaling a zero-update so small (i.e. almost negligible), that the model effectively learns to make a binary choice; a parameter is either worth updating at the cost of some additional rate, or its not updated and the ‘spent’ bits are negligible.
3. Q UANTIZATION
In order to quantize a scalar δ i (denoted by δ in this section to avoid clutter), we use N equispaced bins of width t , and we deﬁne the following quantization function:
[Equation]
As both rounding and clipping are non-differentiable, the gradient of Q t is approximated using the Straight-Through estimator (STE), proposed by Bengio et al.
That is, we assume ∂Q t ( δ ) /∂δ
= 1 .
The bins are intervals Q − 1 t (¯ δ ) =
[¯ δ − t/ 2 , ¯ δ + t/ 2] .
We view t as a hyperparameter, and deﬁne N to be the smallest integer such that the region covered by bins (i.e. the interval [ − ( N − 1) t/ 2 , ( N − 1) t/ 2] ), covers at least 1 − 2 − 8 of the probability mass of p ( δ ) .
Indeed the number of bins N is proportional to the ratio of the width of p ( δ ) and the width of the bins: N ∝ σ/t .
The number of bins presents a tradeoff between ﬁnetuning ﬂexibility and model rate costs Ď
M , so the σ/t -ratio is an important hyperparameter.
The higher N , the higher these costs due to ﬁner quantization, but simultaneously the lower the quantization gap, enabling more ﬂexible ﬁnetuning.
Since ¯ δ = Q t ( δ ) , the discrete model prior p [¯ δ ] is the pushforward of p ( δ ) through Q t :
[Equation]
That is, p [¯ δ ] equals the mass of p ( δ ) in the bin of ¯ δ , which can be computed as the difference of the cumulative density function (CDF) of p ( δ ) evaluated at the edges of that bin.
3. E NTROPY CODING AND DECODING
After ﬁnetuning of the compression model on instance x by minimizing the loss in eq.
(2), both the latents and the model updates are entropy coded (under their respective priors p ¯ θ ( z ) and p [¯ δ ] ) into a bitstream b .
Decoding starts by decoding ¯ δ using p [¯ δ ] , followed by decoding z using p ¯ θ ( z ) (where ¯ θ = ¯ δ + θ D ), and ﬁnally reconstructing ˆ x using p ¯ θ ( x | z ) .
The whole process is shown in Figureand deﬁned formally in Algorithms 1 and 2.
E XPERIMENTAL SETUP 4. D ATASETS
The experiments in this paper use images and videos from the following two datasets:
CLIC19
The CLIC19 dataset contains a collection of natural high resolution images.
It is conventionally divided into a professional set, and a set of mobile phone images.
Here we merge the existing training folds of both sets and use the resulting dataset D to train our I-frame model.
The corresponding validation folds are used to validate the global model performance.
Xiph-5N 2fps
The Xiph dataset contains a variety of videos of different formats.
We select a representative sample of ﬁve videos ( 5N ) from the set of 1080p videos (see Appendix B for details regarding selection of these samples).
Each video is temporally subsampled to 2 fps to create a dataset of I-frames, referred to as Xiph-5N 2fps.
Frames in all videos contain 1920 × 1080 pixels, and the set of I-frames after subsampling to 2 fps contain between 20 and 42 frames.
The ﬁve selected videos are single-scene but multi-shot, and come from a variety of sources.
Xiph-5N 2fps is used to validate our instance-adaptive data compression framework.
4. G LOBAL MODEL ARCHITECTURE AND TRAINING
Model rate can be restricted by (among others) choosing a low-complexity neural compression model, and amortizing the additional model update costs over a large number of pixels.
sender receiver latentprior ! !
(#) shared knowledge encoder % " (#|')
EE 0 00 0 0 1 1!"
ED
EE 0
ED " decoder ! !
('|#) latentprior ! !
(#) decoder ! !
('|#) model prior !
[)δ] # !
E E
E E
D D
D $ $ # !
# "# % $ ⊖ %δ ⊕
%δ E
D
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[)δ] % $
The most natural case for full-model adaptation is therefore to ﬁnetune a low-complexity model on a video instance.
Typical video compression setups combine an image compression (I-frame) model to compress key frames, and a predict- or between-frame model to reconstruct the remaining frames.
Without loss of generality for video-adaptive ﬁnetuning, we showcase our full-model ﬁnetuning framework for the I-frame compression problem, being a subproblem of video compression.
Speciﬁcally, we use the (relatively low-complexity) hyperprior-model proposed by Ball´e et al.
(2018), including the mean-scale prior (without context) from Minnen et al.
Before ﬁnetuning, this model is trained on the training fold of the CLIC19 dataset, using the RD objective given in eq.
Appendix C provides more details on both its architecture and the adopted training procedure.
4. I NSTANCE - ADAPTIVE FINETUNING
Each instance in the Xiph-5N 2fps dataset (i.e. a set of I-frames belonging to one video) is (separately) used for full-model adaptation.
The resulting model rate costs are amortized over the set of
I-frames, rather than the full video.
As a benchmark, we only ﬁnetune the encoding procedure, as it does not induce additional bits in the bitstream.
Encoder-only ﬁnetuning can be implemented by either ﬁnetuning the encoding model parameters φ , or directly optimizing the latents as in Campos et al.
We implement both benchmarks, as the former is an ablated version of our proposed full-model ﬁnetuning, whereas the latter is expected to perform better due to the amortization gap
Cremer et al.
Global models trained with RD tradeoff parameter β ∈ { 3 e − 3 , 1 e − 3 , 2 . 5 e − 4 , 1 e − 4 } are used to initialize the models that are then being ﬁnetuned with the corresonding value for β .
Both encoderonly and direct-latent ﬁnetuning minimize the RD loss as given in eq.
For encoder-only tuning we use a constant learning rate of 1 e − 6 , whereas for latent optimization a learning rate of 5 e − 4 is used for the low bitrate region (i.e. two highest β values), and 1 e − 3 for the high rate region.
In case of direct latent optimization, the pre-quantized latents are ﬁnetuned, which are initialized using a forward pass of the encoder.
Our instance-adaptive full-model ﬁnetuning framework extends encoder-only ﬁnetuning by jointly updating φ and θ using the RDM loss from eq.
In this case we ﬁnetune the global model that is trained with β = 0 .
001 , independent of the value of β during ﬁnetuning.
Empirically, this resulted in negligible difference in performance compared to using the global model of the corresponding ﬁnetuning β , while it alleviated memory constraints thanks to the smaller size of this low bitrate model architecture (see Appendix C).
The training objective in eq.
(2) is expressed in bits per pixel and optimized using a ﬁxed learning rate of 1 e − 4 .
The parameters for the model prior were chosen as follows: quantization bin width t = 0 .
005 , standard deviation σ = 0 .
05 , and the multiplicative factor of the spike α = 1000 .
We empirically found that sensitivity to changing α in the range 50-5000 was low.
Realize that, instead of empirically setting α , its value could also be solved for a target initial cost Ď
M 0 , given the number of decoding parameters and pixels in the ﬁnetuning instance.
All ﬁnetuning experiments (both encoding-only and full-model ) ran for 100 k steps, each containing one mini-batch of a single, full resolution I-frame 3 .
We used the Adam optimizer (default settings) (Kingma & Ba, 2014), and best model selection was based on the RD Ď
M loss over the set of I-frames.
R ESULTS 5. R ATE - DISTORTION GAINS
Figure 2a shows the compression performance for encoder-only ﬁnetuning, direct latent optimization, and full-model ﬁnetuning, averaged over all videos in the Xiph-5N 2fps dataset for different rate-distortion tradeoffs.
Finetuning of the entire parameter-space results in much higher RD Ď
M gains (on average approximately 1 dB for the same bitrate) compared to only ﬁnetuning the encoding parameters φ or the latents directly.
Figure 8 in Appendix E shows this plot for each video separately.
Note that encoder-only ﬁnetuning performance is on par with direct latent optimization, implying that the amortization gap (Cremer et al., 2018) is close to zero when ﬁnetuning the encoder model on a moderate number of I-frames.
Table 1 provides insight in the distribution of bits over latent rate R and model rate Ď
M , which both increase for lower values of β .
However, the relative contribution of the model rate increases for the higher bitrate regime, which could be explained by the fact that in this regime the latent rate can more heavily be reduced by ﬁnetuning.
Figure 2a indeed conﬁrms that the the total rate reduction is higher for the high bitrate regime, which thus fully originates from the reduction in latent rate after ﬁnetuning.
Table 1 also shows that the static intial costs Ď
M 0 only marginally contribute to the total model rate.
Figure 2b shows for one video how ﬁnetuning progressed over training steps, conﬁrming that the compression gains already at the beginning of ﬁnetuning cover these initial costs.
This effect was visible for all tested videos (see Appendix E).
[Figure]
where we show the effect of both quantization- ( Q t ( δ ) ) and model rate aware ( M Loss) ﬁnetuning.
Case VI shows the upper bound on achievable ﬁnetuning performance when (naively) not taking into account quantization and model update rate.
5. A BLATIONS
Figure 2c shows several ablation results for one video.
Case I is our proposed full-model ﬁnetuning, optimizing the RDM loss, while simultaneously quantizing the updates to compute distortion (denoted with Q t ( δ ) ).
One can see that not doing quantization-aware ﬁnetuning (case II) deteriorates the distortion gains during evaluation.
Removing the (continuous) model rate penalty from the ﬁnetuning procedure (case III) imposes an extreme increase in rate during evaluation, caused by unbounded growing of the model rate during ﬁnetuning.
As such, the models result in a rate even much higher than the baseline model’s rate, showing that ﬁnetuning without model rate awareness provides extremely poor results.
Case IV shows performance deterioration in the situation of both quantization- and model rate unaware ﬁnetuning.
Analyzing these runs while (naively) not taking into account the additional model update costs (cases V and VI) provides upper bounds on compression improvement.
Case VI shows the most naive bound; RD ﬁnetuning without quantization, whereas case V is a tighter bound that does include quantization.
The gap between V and VI is small, suggesting that the used quantization strategy does only mildly harm performance.
An ablation study on the effect of the number of ﬁnetuning frames is provided in Appendix D.
It reveals that, under the spike-and-slab model prior, full-model ﬁnetuning works well for a large range of instance lengths.
Only in the worst case scenario, when ﬁnetuning only one frame in the low bitrate regime, full-model ﬁnetuning was found to be too costly.
5. D ISTRIBUTION OF BITS
Figure 3 shows for different (mutually exclusive) parameter groups the distribution of the model updates δ (top) and their corresponding bits (bottom).
Interestingly one can see how for (almost) all groups, the updates are being clipped by our earlier deﬁned quantizer Q t .
This suggests the need for large, expensive updates in these parameter groups, for which the additional RD gain thus appears to outweigh the extra costs.
At the same time, all groups show an elevated center bin, thanks to training with the spike-and-slab prior (see Appendix F).
By design of this prior, the bits paid for this zeroupdate are extremely low, which can best be seen in the bits histogram (Fig.
3-bottom) of the Codec
Decoder Weight and Biases .
The parameter updates of the Codec Decoder IGND group are the only ones that are non-symmetrically distributed across the zero-updated, which can be explained by the fact that IGDN (Ball´e et al., 2016) is an (inverse) normalization layer.
The Codec Decoder Weights were found to contribute most to the total model rate Ď
M .
[Figure]
4 b/px = 0.49 b/param 0.0 2. 4.0 6.0 1e 0.15 0.10 0.050.00 0.05 0.10 0.15 0. 6.0 1e β
PSNR
R + Ď
M
R Ď
M Ď
MĎ M Ď M [Table]
bits
[Table]
[Table]
bits
[Table]
[Table]
D ISCUSSION
This work presented instance-adaptive neural compression, the ﬁrst method that enables ﬁnetuning of a full compression model on a set of I-frames from a single video, while restricting the additional bits for encoding the (quantized) model updates.
To this end, the typical rate-distortion loss was extended by incorporating both model quantization and the additional model rate costs during ﬁnetuning.
This loss guarantees pure RD Ď
M performance gains after overcoming a small initial cost for encoding the zero-update.
We showed improved RD Ď M
performance on all ﬁve tested videos from the Xiph dataset, with an average distortion improvement of approximately 1 dB for the same bitrate.
Among videos, we found a difference in achieved ﬁnetuning RD Ď
M gain (see Appendix E).
Possible causes can be three-fold.
First, the performance of the global model differs per video, therewith inﬂuencing the maximum gains to be achieved by ﬁnetuning.
Second, video characteristics such as (non-)stationarity greatly inﬂuence the diversity of the set of I-frames, thereby affecting the ease of model-adaption.
Third, the number of I-frames differs per video and thus trades off model update costs (which are amortized over the set of I-frames), with ease of ﬁnetuning.
The results of the ablation in Fig.
2c show that the quantization gap (V vs VI) is considerably smaller than the performance deterioration due to (additionally) regularizing the ﬁnetuning using the model prior (I vs V).
Most improvement in future work is thus expected to be gained by leveraging more ﬂexible model priors, e.g. by learning its parameters and/or modeling dependencies among updates.
We showed how instance-adaptive full-model ﬁnetuning greatly improves RD Ď
M performance for
I-frame compression, a subproblem in video compression.
Equivalently, one can exploit full-model ﬁnetuning to enhance compression of the remaining (non-key) frames of a video, compressing the total video even further.
Also, neural video compression models that exploit temporal redundancy, could be ﬁnetuned, as long as the model’s complexity is low enough to restrict model rate.
Leveraging such a low-complexity video model moves computational complexity of data compression from the receiver to the sender by heavily ﬁnetuning this small model on each video.
We foresee convenience of this computational shift in applications where bandwidth and receiver compute power is scarce, but encoding compute time is less important.
This use case in practice happens e.g. for (nonlive) video streaming to low-power edge devices.
Finding such low-complexity video compression models is however non-trivial as it’s capacity must still be enough to compress an entire video.
We foresee great opportunities here, and will therefore investigate this in future work.
A M ODEL RATE LOSS A. G RADIENT DEFINITION
The proof below shows that ∇ δ Ď
M is an unbiased ﬁrst-order approximation of ∇ δ M , validating its use during ﬁnetuning.
The gradient of the continuous model rate loss M towards δ is deﬁned as:
[Equation]
The gradient of the non-differentiable discrete model rate loss ¯ M towards δ can be deﬁned by exploiting the Straight-Through gradient estimator (Bengio et al., 2013), i.e. ∇ δ ¯ δ = 1 .
As such 4 :
[Equation]
By ﬁrst-order approximation we can write:
[Equation]
8) and
[Equation]
Using eq.
(8) and eq.
(9), we can express the gradient of the discrete model rate costs ∇ δ ¯ M as:
[Equation] [Equation]
which is thus a ﬁrst-order approximation of ∇ δ M .
A. C ONTINUOUS VS DISCRETE MODEL RATE PENALTY
The proof provided in the previous section is not restricted to speciﬁc designs for p ( δ ) , and thus holds both for the spike-and-slab prior including a spike ( α > 0 ), or the special case where no spike is used ( α = 0 ).
Figure 4 shows quantization of the model updates (top-left) and its corresponding gradient (leftbottom), exploiting the Straight-Through gradient estimator Bengio et al.
Also the discrete (true) model rate Ď
M and its continuous analogy M (middle-top) with their corresponding gradients (middle-bottom) are shown for the special case of not using a spike, i.e. α = 0 .
One can see that the continuous model rate proxy is a shifted version of the discrete costs.
Since such a translation does not inﬂuence the gradient (and neither gradient-based optimization), the continuous model rate loss can be used during ﬁnetuning, preventing instable training thanks to its smooth gradient.
[Figure]
Figure 4-right shows the same ﬁgure but for a model prior that includes a spike.
Comparing true (discrete) model rate costs of the slab-only prior (middle-top), to these costs using the spike-and-slab prior (right-top), shows how the introduced spike reduces the number of bits to encode the zeroupdate (i.e. the center bin), at the cost of making larger updates more expensive in number of bits.
Another interesting phenomena is visible when comparing the gradients of the discrete and continuous model rates for slab-only (middle-bottom) versus spike-and-slab prior (right-bottom).
The effect of the spike almost fully disappears in the gradient of the discrete model rate.
This is caused by the fact that most of the spike’s mass is (by design) positioned inside the center quantization bin after quantization.
Mathematically, this can be seen from ﬁlling in eq.
(7) for the spike-and-slab prior: ∇ δ
[Equation]
We can distinguish different behavior of eq.
(11) for two distinct ranges of ¯ δ :
• ¯ δ =Symmetry
around
zero-update: p slab ¯ δ + t
[Equation] = p
slab
[Equation]
¯
[Equation]
and p spike (¯ δ
[Equation]
p
[Equation]
• ¯ δ (cid:54) =
[Equation]
Since t = 6 σ spike :
[Equation]
Note that this approximation becomes less tight for large α , as the small probabilities and cumulative densities of the spike distribution are multiplied by α .
From the ¯ δ = 0 case, one can see that inclusion of the spike leaves the gradient unbiased.
The ¯ δ (cid:54) = 0 case shows that the spike does not inﬂuence the gradient in the limit when the spike’s mass is entirely positioned within the center quantization bin.
As the standard deviation of the spiky
Gaussian was chosen to be t 6 , a total of 0 .
3% of it’s mass is in practice being quantized in an offcenter quantization bin.
This explains the slight increase of the off-center bins in the gradient of the discrete model rate costs in Fig.
4 (right-bottom).
Comparing the gradient of the discrete versus continuous model rate costs for the spike-and-slab prior in Fig.
4 (right-bottom), we can see that the ﬁrst order approximation between the two introduces a larger error than in the slab-only case (Fig.
4(middle-bottom)).
This can be explained by the fact that the introduced spiky Gaussian has a higher tangent due to its support being more narrow than that of the Gaussian slab.
Nevertheless, the use of the continuous model rate loss is preferred for ﬁnetuning as it (much more) strictly enforces zeroupdates (thanks to the present gradient peaks around the center bin) than its discrete counterpart.
B S AMPLE SELECTION FROM X IPH DATASET B. X IPH DATASET
The Xiph test videos can be found at https://media.xiph.org/video/derf/ .
Like Rippel et al.
we select all 1080p videos, and exclude computer-generated videos and videos with inappropriate licences, which leaves us with the following videos:
[Table]
B. X IPH -5N 2 FPS DATASET
Due to computational limits, we draw a sample of ﬁve videos, which we refer to as the Xiph-5N dataset.
When drawing such a small sample randomly, a high probability arises of drawing an unrepresentative sample, including for example too many videos with either low or high ﬁnetuning potential.
To alleviate this problem, we use the following heuristic to select N (= 5) videos:
Evaluate the global model’s RD performance on all videos for all values of β . 2.
Per β value, rank all videos based on their respective L RD loss.
For each video, average the L RD rank over all values of β . 4.
Order all videos according to their average rank, and select N videos based on N + 1 evenly spaced percentiles.
The global model’s RD performance for all videos from the Xiph dataset is shown in Figure 5.
The ﬁve videos part of Xiph-5N are indicated with colors, and Table 2 provides more details about these ﬁve videos.
The column titled RD-tank percentile shows the actual percentile at which the selected videos are ranked.
The computed (target) percentiles for N
[Figure]
Video
[Table]
in to tree
[Table]
C G LOBAL MODEL ARCHITECTURE AND TRAINING
For our neural compression model, we adopt the architecture proposed by Ball´e et al.
(2018), including the mean-scale prior from Minnen et al.
We use a shared hyperdecoder to predict the mean and scale parameters.
Like (Ball´e et al., 2018)
we use a model architecture with fewer parameters for the low bitrate regime ( β ≥ 1 e − 3 ).
Table 3 indicates both the model architecture and the number of parameters, grouped per sub-model.
The upper row in this table links the terminology proposed by Ball´e et al.
(2018) to conventional VAE terminology which we follow in this work.
Figure 6 provides a visual overview of the model architecture, where we use z 2 and z 1 to indicate the latent and hyper-latent space respectively (referred to as y and z in the original paper of Ball´e et al.
Note that even though we adopt a hierarchical latent variable model, we simplify the notation by deﬁning a single latent space
z = { z 1 , z 2 } throughout this work.
During training we adopt a mixed quantization strategy, where the quantized latent z 2 are used to calculate the distortion loss (with their gradients estimated using the Straight-Trough estimator from
Bengio et al.
(2013)) , while we use noisy samples for z 1 , z 2 when computing the rate loss.
Optimization of eq.
(1) on the training fold of the CLIC19 dataset, was done using the Adam optimizer with default settings (Kingma & Ba, 2014), and took 1.5M steps for the low bitrate models, and 2.0M steps for the high bitrate models.
Each step contained 8 random crops of 256 × 256 pixels, and the initial learning rate was set to 1 e − 4 and lowered to 1 e − 5 at 90% of training.
[Table]
[Equation]
[Table]
[Figure]
[Table]
Low bitrate model
[Table]
channels
[Table]
Parameter
[Table]
High bitrate model
[Table]
26k
[Table]
D T EMPORAL A BLATION
In this experiment we investigate the tradeoff between the number of frames that the model is ﬁnetuned on, and the ﬁnal RD Ď
M performance.
The higher this number of frames, the higher (potentially) the diversity (making ﬁnetuning more difﬁcult), but the lower the bitrate overhead (in bits/pixels) due to model updates.
This ablation repeats our main experiment on the sunflower video (Fig.
8) for a varying number of I-frames.
We sample f number of frames (equispaced) from the full video, starting at the zero’th index.
The experiment is run for f ∈ { 1 , 2 , 5 , 10 , 25 , 50 , 100 , 250 , 500 } , and the two outmost rate-distortion tradeoffs: β ∈ { 0 . 003 , 0 . 0001 } .
Note that the original experiment was done with frames sampled at 2 fps, resulting in f = 42 for the sunflower video.
[Figure]
Figure 7 shows (for the low and high bitrate region) the total RD Ď
M loss, and its subdivision in distortion and the different rate terms, as a function of numbers of ﬁnetuning frames.
Full-model ﬁnetuning outperforms encoder-only ﬁnetuning in all cases, except for f = 1 in the low bit-rate regime.
In this case, the model rate Ď
M is causing the total rate R + Ď
M to become too high to be competitive with the baselines.
This in turn is mainly caused by the initial cost Ď
M 0 , which can only be amortized over a single frame.
In general, for other values of f , these initial costs were found to contribute only little to the total rate (with even a negligible contribution in the low and high bitrate regions for respectively f ≥ 25 and f ≥ 10 ).
Note that the global model’s performance varies noticeably as a function of f .
Apparently, the ﬁrst frame of this video is easy to compress, therewith lowering the total loss for small sets of I-frames.
To make fair comparisons, one should thus only consider relative performance of encoder-only and full-model ﬁnetuning with respect to the RD trained baseline.
In line with our main ﬁndings in Fig.
2c, full-model ﬁnetuning shows the biggest improvements for the high bitrate setting.
Interestingly, when comparing the low and high bitrate regimes, the total relative RD Ď
M gain of full-model ﬁnetuning follows a similar pattern for varying values of f (higher gain for higher f ).
However, the subdivision of this gain in rate and distortion gain differs due to leveraging another tradeoff setting β .
For the high bitrate, mainly distortion is diminished (row 2), whereas for the low bitrate, rate is predominantly reduced (row 3).
These rate and distortion reduction plots clearly show how the ﬂexibility of full-model (compared to encoder-only) ﬁnetuning can improve results in various conditions.
This experiment has shown that the potential for full-model ﬁnetuning (under the current model architecture and prior) seems highest for video compression purposes, as gains are negative (due to relative high static initial costs in the low bitrate regime) or only marginal (in the high bitrate regime) when overﬁtting on a single frame.
Yet, we hypothesize that full-model ﬁnetuning could still be useful for (single) image compression as well, given other choices for the model architecture and/or model prior.
Also, the provided ablation is run on one video only, so further research is needed to investigate full-model ﬁnetuning in an image compression setup.
E R ATE - DISTORTION FINETUNING PERFORMANCE PER VIDEO
Figure 8 shows the RD Ď
M plots for the different videos after ﬁnetuning for 100k steps.
Full-model ﬁnetuning outperforms the global model, encoder-only and direct latent optimization for all videos.
The blue lines, indicating global model performance, differ per video, which might inﬂuence the ﬁnetuning gains, which also differ per video, e.g. controlled burn versus sunflower .
True entropy-coded results are used to create these graphs, rather than
the computed RD Ď
M values.
Deviations between entropy-coded and computed rates were found to be negligible (mean deviation was 1.94e-04 bits/pixel for R and 1.06e-03 bits/pixel for ¯ M ).
Throughout this paper, all training graphs and ablations are therefore provided using the computed values, rather than the entropy coded results.
Figure 9 shows for each of these videos the ﬁnetuning progression over training steps.
Also here, differences in performance are visible among videos.
The videos that result in highest ﬁnetuning gains, e.g. sunflower , show quicker performance improvement after the start of ﬁnetuning, and also continue to improve more over time.
[Figure]
[Figure]
F M ODEL U PDATES D ISTRIBUTIONS
Figure 10 shows how the (quantized) model updates become much sparser (top row) when ﬁnetuning includes the spike-and-slab model rate loss M , compared to unregularized ﬁnetuning (bottom row).