191 research outputs found
Data compression and computational efficiency
In this thesis we seek to make advances towards the goal of effective learned compression. This entails using machine learning models as the core constituent of compression algorithms, rather than hand-crafted components.
To that end, we first describe a new method for lossless compression. This method
allows a class of existing machine learning models β latent variable models β to be
turned into lossless compressors. Thus many future advancements in the field of
latent variable modelling can be leveraged in the field of lossless compression. We
demonstrate a proof-of-concept of this method on image compression. Further, we
show that it can scale to very large models, and image compression problems which
closely resemble the real-world use cases that we seek to tackle.
The use of the above compression method relies on executing a latent variable
model. Since these models can be large in size and slow to run, we consider how
to mitigate these computational costs. We show that by implementing much of the
models using binary precision parameters, rather than floating-point precision, we
can still achieve reasonable modelling performance but requiring a fraction of the
storage space and execution time.
Lastly, we consider how learned compression can be applied to 3D scene data - a
data medium increasing in prevalence, and which can require a significant amount of
space. A recently developed class of machine learning models - scene representation
functions - has demonstrated good results on modelling such 3D scene data. We
show that by compressing these representation functions themselves we can achieve
good scene reconstruction with a very small model size
Universal Deep Image Compression via Content-Adaptive Optimization with Adapters
Deep image compression performs better than conventional codecs, such as
JPEG, on natural images. However, deep image compression is learning-based and
encounters a problem: the compression performance deteriorates significantly
for out-of-domain images. In this study, we highlight this problem and address
a novel task: universal deep image compression. This task aims to compress
images belonging to arbitrary domains, such as natural images, line drawings,
and comics. To address this problem, we propose a content-adaptive optimization
framework; this framework uses a pre-trained compression model and adapts the
model to a target image during compression. Adapters are inserted into the
decoder of the model. For each input image, our framework optimizes the latent
representation extracted by the encoder and the adapter parameters in terms of
rate-distortion. The adapter parameters are additionally transmitted per image.
For the experiments, a benchmark dataset containing uncompressed images of four
domains (natural images, line drawings, comics, and vector arts) is constructed
and the proposed universal deep compression is evaluated. Finally, the proposed
model is compared with non-adaptive and existing adaptive compression models.
The comparison reveals that the proposed model outperforms these. The code and
dataset are publicly available at https://github.com/kktsubota/universal-dic.Comment: Accepted at the IEEE/CVF Winter Conference on Applications of
Computer Vision (WACV) 202
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