9,488 research outputs found
A bio-inspired image coder with temporal scalability
We present a novel bio-inspired and dynamic coding scheme for static images.
Our coder aims at reproducing the main steps of the visual stimulus processing
in the mammalian retina taking into account its time behavior. The main novelty
of this work is to show how to exploit the time behavior of the retina cells to
ensure, in a simple way, scalability and bit allocation. To do so, our main
source of inspiration will be the biologically plausible retina model called
Virtual Retina. Following a similar structure, our model has two stages. The
first stage is an image transform which is performed by the outer layers in the
retina. Here it is modelled by filtering the image with a bank of difference of
Gaussians with time-delays. The second stage is a time-dependent
analog-to-digital conversion which is performed by the inner layers in the
retina. Thanks to its conception, our coder enables scalability and bit
allocation across time. Also, our decoded images do not show annoying artefacts
such as ringing and block effects. As a whole, this article shows how to
capture the main properties of a biological system, here the retina, in order
to design a new efficient coder.Comment: 12 pages; Advanced Concepts for Intelligent Vision Systems (ACIVS
2011
Streaming an image through the eye: The retina seen as a dithered scalable image coder
We propose the design of an original scalable image coder/decoder that is
inspired from the mammalians retina. Our coder accounts for the time-dependent
and also nondeterministic behavior of the actual retina. The present work
brings two main contributions: As a first step, (i) we design a deterministic
image coder mimicking most of the retinal processing stages and then (ii) we
introduce a retinal noise in the coding process, that we model here as a dither
signal, to gain interesting perceptual features. Regarding our first
contribution, our main source of inspiration will be the biologically plausible
model of the retina called Virtual Retina. The main novelty of this coder is to
show that the time-dependent behavior of the retina cells could ensure, in an
implicit way, scalability and bit allocation. Regarding our second
contribution, we reconsider the inner layers of the retina. We emit a possible
interpretation for the non-determinism observed by neurophysiologists in their
output. For this sake, we model the retinal noise that occurs in these layers
by a dither signal. The dithering process that we propose adds several
interesting features to our image coder. The dither noise whitens the
reconstruction error and decorrelates it from the input stimuli. Furthermore,
integrating the dither noise in our coder allows a faster recognition of the
fine details of the image during the decoding process. Our present paper goal
is twofold. First, we aim at mimicking as closely as possible the retina for
the design of a novel image coder while keeping encouraging performances.
Second, we bring a new insight concerning the non-deterministic behavior of the
retina.Comment: arXiv admin note: substantial text overlap with arXiv:1104.155
Low computational complexity variable block size (VBS) partitioning for motion estimation using the Walsh Hadamard transform (WHT)
Variable Block Size (VBS) based motion estimation has
been adapted in state of the art video coding, such as
H.264/AVC, VC-1. However, a low complexity H.264/AVC
encoder cannot take advantage of VBS due to its power consumption
requirements. In this paper, we present a VBS partition
algorithm based on a binary motion edge map without
either initial motion estimation or Rate-Distortion (R-D)
optimization for selecting modes. The proposed algorithm
uses the Walsh Hadamard Transform (WHT) to create a binary
edge map, which provides a computational complexity
cost effectiveness compared to other light segmentation
methods typically used to detect the required region
Perceptual Copyright Protection Using Multiresolution Wavelet-Based Watermarking And Fuzzy Logic
In this paper, an efficiently DWT-based watermarking technique is proposed to
embed signatures in images to attest the owner identification and discourage
the unauthorized copying. This paper deals with a fuzzy inference filter to
choose the larger entropy of coefficients to embed watermarks. Unlike most
previous watermarking frameworks which embedded watermarks in the larger
coefficients of inner coarser subbands, the proposed technique is based on
utilizing a context model and fuzzy inference filter by embedding watermarks in
the larger-entropy coefficients of coarser DWT subbands. The proposed
approaches allow us to embed adaptive casting degree of watermarks for
transparency and robustness to the general image-processing attacks such as
smoothing, sharpening, and JPEG compression. The approach has no need the
original host image to extract watermarks. Our schemes have been shown to
provide very good results in both image transparency and robustness.Comment: 13 pages, 7 figure
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