651 research outputs found
Deep Learning frameworks for Image Quality Assessment
Technology is advancing by the arrival of deep learning and it finds huge application in image
processing also. Deep learning itself sufficient to perform over all the statistical methods. As a
research work, I implemented image quality assessment techniques using deep learning. Here I
proposed two full reference image quality assessment algorithms and two no reference image quality
algorithms. Among the two algorithms on each method, one is in a supervised manner and other is
in an unsupervised manner.
First proposed method is the full reference image quality assessment using autoencoder. Existing
literature shows that statistical features of pristine images will get distorted in presence of distortion.
It will be more advantageous if algorithm itself learns the distortion discriminating features. It will
be more complex if the feature length is more. So autoencoder is trained using a large number of
pristine images. An autoencoder will give the best lower dimensional representation of the input.
It is showed that encoded distance features have good distortion discrimination properties. The
proposed algorithm delivers competitive performance over standard databases.
If we are giving both reference and distorted images to the model and the model learning itself
and gives the scores will reduce the load of extracting features and doing post-processing. But model
should be capable one for discriminating the features by itself. Second method which I proposed is
a full reference and no reference image quality assessment using deep convolutional neural networks.
A network is trained in a supervised manner with subjective scores as targets. The algorithm is
performing e�ciently for the distortions that are learned while training the model.
Last proposed method is a classiffication based no reference image quality assessment. Distortion
level in an image may vary from one region to another region. We may not be able to view distortion
in some part but it may be present in other parts. A classiffication model is able to tell whether a
given input patch is of low quality or high quality. It is shown that aggregate of the patch quality
scores is having a high correlation with the subjective scores
Entropy in Image Analysis II
Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas
Self-Supervised Blind Source Separation via Multi-Encoder Autoencoders
The task of blind source separation (BSS) involves separating sources from a
mixture without prior knowledge of the sources or the mixing system. This is a
challenging problem that often requires making restrictive assumptions about
both the mixing system and the sources. In this paper, we propose a novel
method for addressing BSS of non-linear mixtures by leveraging the natural
feature subspace specialization ability of multi-encoder autoencoders with
fully self-supervised learning without strong priors. During the training
phase, our method unmixes the input into the separate encoding spaces of the
multi-encoder network and then remixes these representations within the decoder
for a reconstruction of the input. Then to perform source inference, we
introduce a novel encoding masking technique whereby masking out all but one of
the encodings enables the decoder to estimate a source signal. To this end, we
also introduce a so-called pathway separation loss that encourages sparsity
between the unmixed encoding spaces throughout the decoder's layers and a
so-called zero reconstruction loss on the decoder for coherent source
estimations. In order to carefully evaluate our method, we conduct experiments
on a toy dataset and with real-world biosignal recordings from a
polysomnography sleep study for extracting respiration.Comment: 17 pages, 8 figures, submitted to Information Science
An Introduction to Neural Data Compression
Neural compression is the application of neural networks and other machine
learning methods to data compression. Recent advances in statistical machine
learning have opened up new possibilities for data compression, allowing
compression algorithms to be learned end-to-end from data using powerful
generative models such as normalizing flows, variational autoencoders,
diffusion probabilistic models, and generative adversarial networks. The
present article aims to introduce this field of research to a broader machine
learning audience by reviewing the necessary background in information theory
(e.g., entropy coding, rate-distortion theory) and computer vision (e.g., image
quality assessment, perceptual metrics), and providing a curated guide through
the essential ideas and methods in the literature thus far
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