514 research outputs found
Improved Lossy Image Compression with Priming and Spatially Adaptive Bit Rates for Recurrent Networks
We propose a method for lossy image compression based on recurrent,
convolutional neural networks that outperforms BPG (4:2:0 ), WebP, JPEG2000,
and JPEG as measured by MS-SSIM. We introduce three improvements over previous
research that lead to this state-of-the-art result. First, we show that
training with a pixel-wise loss weighted by SSIM increases reconstruction
quality according to several metrics. Second, we modify the recurrent
architecture to improve spatial diffusion, which allows the network to more
effectively capture and propagate image information through the network's
hidden state. Finally, in addition to lossless entropy coding, we use a
spatially adaptive bit allocation algorithm to more efficiently use the limited
number of bits to encode visually complex image regions. We evaluate our method
on the Kodak and Tecnick image sets and compare against standard codecs as well
recently published methods based on deep neural networks
A statistical reduced-reference method for color image quality assessment
Although color is a fundamental feature of human visual perception, it has
been largely unexplored in the reduced-reference (RR) image quality assessment
(IQA) schemes. In this paper, we propose a natural scene statistic (NSS)
method, which efficiently uses this information. It is based on the statistical
deviation between the steerable pyramid coefficients of the reference color
image and the degraded one. We propose and analyze the multivariate generalized
Gaussian distribution (MGGD) to model the underlying statistics. In order to
quantify the degradation, we develop and evaluate two measures based
respectively on the Geodesic distance between two MGGDs and on the closed-form
of the Kullback Leibler divergence. We performed an extensive evaluation of
both metrics in various color spaces (RGB, HSV, CIELAB and YCrCb) using the TID
2008 benchmark and the FRTV Phase I validation process. Experimental results
demonstrate the effectiveness of the proposed framework to achieve a good
consistency with human visual perception. Furthermore, the best configuration
is obtained with CIELAB color space associated to KLD deviation measure
Generative Compression
Traditional image and video compression algorithms rely on hand-crafted
encoder/decoder pairs (codecs) that lack adaptability and are agnostic to the
data being compressed. Here we describe the concept of generative compression,
the compression of data using generative models, and suggest that it is a
direction worth pursuing to produce more accurate and visually pleasing
reconstructions at much deeper compression levels for both image and video
data. We also demonstrate that generative compression is orders-of-magnitude
more resilient to bit error rates (e.g. from noisy wireless channels) than
traditional variable-length coding schemes
A Detail Based Method for Linear Full Reference Image Quality Prediction
In this paper, a novel Full Reference method is proposed for image quality
assessment, using the combination of two separate metrics to measure the
perceptually distinct impact of detail losses and of spurious details. To this
purpose, the gradient of the impaired image is locally decomposed as a
predicted version of the original gradient, plus a gradient residual. It is
assumed that the detail attenuation identifies the detail loss, whereas the
gradient residuals describe the spurious details. It turns out that the
perceptual impact of detail losses is roughly linear with the loss of the
positional Fisher information, while the perceptual impact of the spurious
details is roughly proportional to a logarithmic measure of the signal to
residual ratio. The affine combination of these two metrics forms a new index
strongly correlated with the empirical Differential Mean Opinion Score (DMOS)
for a significant class of image impairments, as verified for three independent
popular databases. The method allowed alignment and merging of DMOS data coming
from these different databases to a common DMOS scale by affine
transformations. Unexpectedly, the DMOS scale setting is possible by the
analysis of a single image affected by additive noise.Comment: 15 pages, 9 figures. Copyright notice: The paper has been accepted
for publication on the IEEE Trans. on Image Processing on 19/09/2017 and the
copyright has been transferred to the IEE
Visually Lossless Perceptual Image Coding Based on Natural- Scene Masking Models
Perceptual coding is a subdiscipline of image and video coding that uses models of human visual perception to achieve improved compression efficiency. Nearly, all image and video coders have included some perceptual coding strategies, most notably visual masking. Today, modern coders capitalize on various basic forms of masking such as the fact that distortion is harder to see in very dark and very bright regions, in regions with higher frequency content, and in temporal regions with abrupt changes. However, beyond these obvious forms of masking, there are many other masking phenomena that occur (and co-occur) when viewing natural imagery. In this chapter, we present our latest research in perceptual image coding using natural-scene masking models. We specifically discuss: (1) how to predict local distortion visibility using improved natural-scene masking models and (2) how to apply the models to high efficiency video coding (HEVC). As we will demonstrate, these techniques can offer 10–20% fewer bits than baseline HEVC in the ultra-high-quality regime
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