70,390 research outputs found
Practical Full Resolution Learned Lossless Image Compression
We propose the first practical learned lossless image compression system,
L3C, and show that it outperforms the popular engineered codecs, PNG, WebP and
JPEG 2000. At the core of our method is a fully parallelizable hierarchical
probabilistic model for adaptive entropy coding which is optimized end-to-end
for the compression task. In contrast to recent autoregressive discrete
probabilistic models such as PixelCNN, our method i) models the image
distribution jointly with learned auxiliary representations instead of
exclusively modeling the image distribution in RGB space, and ii) only requires
three forward-passes to predict all pixel probabilities instead of one for each
pixel. As a result, L3C obtains over two orders of magnitude speedups when
sampling compared to the fastest PixelCNN variant (Multiscale-PixelCNN).
Furthermore, we find that learning the auxiliary representation is crucial and
outperforms predefined auxiliary representations such as an RGB pyramid
significantly.Comment: Updated preprocessing and Table 1, see A.1 in supplementary. Code and
models: https://github.com/fab-jul/L3C-PyTorc
Learning Convolutional Networks for Content-weighted Image Compression
Lossy image compression is generally formulated as a joint rate-distortion
optimization to learn encoder, quantizer, and decoder. However, the quantizer
is non-differentiable, and discrete entropy estimation usually is required for
rate control. These make it very challenging to develop a convolutional network
(CNN)-based image compression system. In this paper, motivated by that the
local information content is spatially variant in an image, we suggest that the
bit rate of the different parts of the image should be adapted to local
content. And the content aware bit rate is allocated under the guidance of a
content-weighted importance map. Thus, the sum of the importance map can serve
as a continuous alternative of discrete entropy estimation to control
compression rate. And binarizer is adopted to quantize the output of encoder
due to the binarization scheme is also directly defined by the importance map.
Furthermore, a proxy function is introduced for binary operation in backward
propagation to make it differentiable. Therefore, the encoder, decoder,
binarizer and importance map can be jointly optimized in an end-to-end manner
by using a subset of the ImageNet database. In low bit rate image compression,
experiments show that our system significantly outperforms JPEG and JPEG 2000
by structural similarity (SSIM) index, and can produce the much better visual
result with sharp edges, rich textures, and fewer artifacts
Deep data compression for approximate ultrasonic image formation
In many ultrasonic imaging systems, data acquisition and image formation are
performed on separate computing devices. Data transmission is becoming a
bottleneck, thus, efficient data compression is essential. Compression rates
can be improved by considering the fact that many image formation methods rely
on approximations of wave-matter interactions, and only use the corresponding
part of the data. Tailored data compression could exploit this, but extracting
the useful part of the data efficiently is not always trivial. In this work, we
tackle this problem using deep neural networks, optimized to preserve the image
quality of a particular image formation method. The Delay-And-Sum (DAS)
algorithm is examined which is used in reflectivity-based ultrasonic imaging.
We propose a novel encoder-decoder architecture with vector quantization and
formulate image formation as a network layer for end-to-end training.
Experiments demonstrate that our proposed data compression tailored for a
specific image formation method obtains significantly better results as opposed
to compression agnostic to subsequent imaging. We maintain high image quality
at much higher compression rates than the theoretical lossless compression rate
derived from the rank of the linear imaging operator. This demonstrates the
great potential of deep ultrasonic data compression tailored for a specific
image formation method.Comment: IEEE International Ultrasonics Symposium 202
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