61,221 research outputs found
Study of machine learning techniques for image compression
In the age of the Internet and cloud-based applications, image compression has become increasingly important. Moreover, image processing has recently sparked the interest of technology companies as autonomous machines powered by artificial intelligence using images as input are rapidly growing. Reducing the amount of information needed to represent an image is key to reducing the amount of storage space, transmission bandwidth, and computation time required to process the image, which in turn saves resources, energy, and money. This study aims to investigate machine learning techniques (Fourier, wavelets, and PCA) for image compression. Several Fourier and wavelet methods are included, such as the wellknown Cooley-Tukey algorithm, the discrete cosine transform, and the Mallart algorithm, among others. To comprehend each step of image compression, an object-oriented Matlab code has been developed in-house. To do so, extensive research in machine learning techniques, not only in terms of theoretical understanding, but also in the mathematics that support it. The developed code is used to compare the performance of the different compression techniques studied. The findings of this study are consistent with the advances in image compression technologies in recent years, with the dominance of the JPEG compression method (Fourier) and later JPEG2000 (wavelets) reigning supreme
Roadmap on optical security
Postprint (author's final draft
Fast object detection in compressed JPEG Images
Object detection in still images has drawn a lot of attention over past few
years, and with the advent of Deep Learning impressive performances have been
achieved with numerous industrial applications. Most of these deep learning
models rely on RGB images to localize and identify objects in the image.
However in some application scenarii, images are compressed either for storage
savings or fast transmission. Therefore a time consuming image decompression
step is compulsory in order to apply the aforementioned deep models. To
alleviate this drawback, we propose a fast deep architecture for object
detection in JPEG images, one of the most widespread compression format. We
train a neural network to detect objects based on the blockwise DCT (discrete
cosine transform) coefficients {issued from} the JPEG compression algorithm. We
modify the well-known Single Shot multibox Detector (SSD) by replacing its
first layers with one convolutional layer dedicated to process the DCT inputs.
Experimental evaluations on PASCAL VOC and industrial dataset comprising images
of road traffic surveillance show that the model is about faster than
regular SSD with promising detection performances. To the best of our
knowledge, this paper is the first to address detection in compressed JPEG
images
Semantic Perceptual Image Compression using Deep Convolution Networks
It has long been considered a significant problem to improve the visual
quality of lossy image and video compression. Recent advances in computing
power together with the availability of large training data sets has increased
interest in the application of deep learning cnns to address image recognition
and image processing tasks. Here, we present a powerful cnn tailored to the
specific task of semantic image understanding to achieve higher visual quality
in lossy compression. A modest increase in complexity is incorporated to the
encoder which allows a standard, off-the-shelf jpeg decoder to be used. While
jpeg encoding may be optimized for generic images, the process is ultimately
unaware of the specific content of the image to be compressed. Our technique
makes jpeg content-aware by designing and training a model to identify multiple
semantic regions in a given image. Unlike object detection techniques, our
model does not require labeling of object positions and is able to identify
objects in a single pass. We present a new cnn architecture directed
specifically to image compression, which generates a map that highlights
semantically-salient regions so that they can be encoded at higher quality as
compared to background regions. By adding a complete set of features for every
class, and then taking a threshold over the sum of all feature activations, we
generate a map that highlights semantically-salient regions so that they can be
encoded at a better quality compared to background regions. Experiments are
presented on the Kodak PhotoCD dataset and the MIT Saliency Benchmark dataset,
in which our algorithm achieves higher visual quality for the same compressed
size.Comment: Accepted to Data Compression Conference, 11 pages, 5 figure
Automated Pruning for Deep Neural Network Compression
In this work we present a method to improve the pruning step of the current
state-of-the-art methodology to compress neural networks. The novelty of the
proposed pruning technique is in its differentiability, which allows pruning to
be performed during the backpropagation phase of the network training. This
enables an end-to-end learning and strongly reduces the training time. The
technique is based on a family of differentiable pruning functions and a new
regularizer specifically designed to enforce pruning. The experimental results
show that the joint optimization of both the thresholds and the network weights
permits to reach a higher compression rate, reducing the number of weights of
the pruned network by a further 14% to 33% compared to the current
state-of-the-art. Furthermore, we believe that this is the first study where
the generalization capabilities in transfer learning tasks of the features
extracted by a pruned network are analyzed. To achieve this goal, we show that
the representations learned using the proposed pruning methodology maintain the
same effectiveness and generality of those learned by the corresponding
non-compressed network on a set of different recognition tasks.Comment: 8 pages, 5 figures. Published as a conference paper at ICPR 201
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