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

A NOVEL BIO-INSPIRED STATIC IMAGE COMPRESSION SCHEME FOR NOISY DATA TRANSMISSION OVER LOW-BANDWIDTH CHANNELS

International audienceWe present a novel bio-inspired static image compression scheme. Our model is a combination of a simplified spiking retina model and well known data compression techniques. The fundamental hypothesis behind this work is that the mammalian retina generates an efficient neural code associated to the visual flux. The main novelty of this work is to show how this neural code can be exploited in the context of still image compression. Our model has three main stages. The first stage is the bio-inspired retina model proposed by Thorpe et al [1, 2], which transforms an image into a wave of spikes. This transform is based on the so-called rank order coding. In the second stage, we show how this wave of spikes can be expressed using a 4-ary dictionary alphabet, through a stack run coder. The third stage consists of applying a first order arithmetic coder to the stack run coded signal. We compare our results to JPEG standards and we show that our model has comparable performance for lower computational cost under strong bit rate restrictions when data is highly contaminated with noise. In addition, our model offers scalability for monitoring data transmission flow. The subject matter presented highlights a variety of important issues in the conception of novel bio-inspired compression schemes and additionally presents many potential avenues for future research efforts

Regulation of durum wheat Na(+)/H (+) exchanger TdSOS1 by phosphorylation

We have identified a plasma membrane Na+/H+ exchanger from durum wheat, designated TdSOS1. Heterologous expression of TdSOS1 in a yeast strain lacking endogenous Na+ efflux proteins showed complementation of the Na+- and Li+-sensitive phenotype by a mechanism involving cation efflux. Salt tolerance conferred by TdSOS1 was maximal when co-expressed with the Arabidopsis protein kinase complex SOS2/SOS3. In vitro phosphorylation of TdSOS1 with a hyperactive form of the Arabidopsis SOS2 kinase (T/DSOS2∆308) showed the importance of two essential serine residues at the C-terminal hydrophilic tail (S1126, S1128). Mutation of these two serine residues to alanine decreased the phosphorylation of TdSOS1 by T/DSOS2∆308 and prevented the activation of TdSOS1. In addition, deletion of the C-terminal domain of TdSOS1 encompassing serine residues at position 1126 and 1128 generated a hyperactive form that had maximal sodium exclusion activity independent from the regulatory SOS2/SOS3 complex. These results are consistent with the presence of an auto-inhibitory domain at the C-terminus of TdSOS1 that mediates the activation of TdSOS1 by the protein kinase SOS2. Expression of TdSOS1 mRNA in young seedlings of the durum wheat variety Om Rabia3, using different abiotic stresses (ionic and oxidative stress) at different times of exposure, was monitored by RT–PCR.Peer Reviewe

ANOTHER LOOK AT THE RETINA AS AN IMAGE SCALAR QUANTIZER

International audienceWe investigate, in this paper, the processing of stimuli in the mammalians retina, and raise the analogy between the biological mechanisms involved and already existing analog-to-digital converters functioning. Besides, we propose a possible decoding procedure for the retina neural code under the restrictions of the model presented. The coder/decoder, we describe here, focuses on the temporal behavior of the three last retina layers. As time goes, our system gradually changes from a quasi-uniform quantizer to a highly non-linear one. Besides, high magnitude stimuli are well refined, while small magnitudes are coarsely approximated. This yields an original bioinspired quantization system, the behavior of which evolves dynamically during the time interval of stimuli observation. Here, we present a biologically realistic retina model adapted to a temporal signal. Then, we explore the input/output map of the system and its ability to recover the original signal. Further, we make the parallel between this bioinspired system and well known compandor/quantizer systems used for analog-to-digital converters. Finally, we compare the performance of our quantizer to the dead zone uniform scalar quantizer used in JPEG2000, and show a slightly better behavior for low rate transmissions

Spike based neural codes : towards a novel bio-inspired still image coding schema

We asked whether rank order coding could be used to define an efficient compression scheme for still images. The main hypothesis underlying this work is that the mammalians retina generates a compressed neural code for the visual stimuli. The main novelty of our approach is to show how this neural code can be exploited in the context of image compression. Our coding scheme is a combination of a simplified spiking retina model and well known data compression techniques and consists in three main stages. The first stage is the bio-inspired retina model proposed by Thorpe et al. This model transforms of a stimulus into a wave of electrical impulses called spikes. The major property of this retina model is that spikes are ordered in time as a function of the cells activation: this yields the so-called rank order code (ROC). ROC states that the first wave of spikes give a good estimate of the input signal. In the second stage, we show how this wave of spikes can be expressed using a 4-ary dictionary alphabet: the stack run coding. The third stage consists in applying, to the stack run code, a arithmetic coder of the first order. We then compare our results to the JPEG standards and we show that our model offers similar rate/quality trade-off until 0.07 bpp, for a lower computational cost. In addition, our model offers interesting properties of scalability and of robustness to noise

ENCODING AND DECODING STIMULI USING A BIOLOGICALLY REALISTIC MODEL: THE NON-DETERMINISM IN SPIKE TIMINGS SEEN AS A DITHER SIGNAL

International audienceThe mammalians retina conveys information by means of spike trains. Though, understanding the way these spike trains represent the stimuli is still a challenging issue, especially when considering their non-determinism. Interestingly, the spike-based code of the retina is binary-like, and thus we considered its study in a signal coding fashion. To do so, we specied a coding scheme based on the mean ring rate of spikes simulated by a realistic model of the mammalians retina. We, then, inverted the generated code to reconstruct the original input. Besides, we established some links between the processing occurring in the retina and state-of-the art methods in pure image coding. Finally, we gave a biologically plausible interpretation for the non-determinism in the spike ring timings

Frames for Exact Inversion of the Rank Order Coder

International audienceOur goal is to revisit rank order coding by proposing an original exact decoding procedure for it. Rank order coding was proposed by Thorpe . who stated that the order in which the retina cells are activated encodes for the visual stimulus. Based on this idea, the authors proposed in a rank order coder/decoder associated to a retinal model. Though, it appeared that the decoding procedure employed yields reconstruction errors that limit the model bit-cost/quality performances when used as an image codec. The attempts made in the literature to overcome this issue are time consuming and alter the coding procedure, or are lacking mathematical support and feasibility for standard size images. Here we solve this problem in an original fashion by using the frames theory, where a frame of a vector space designates an extension for the notion of basis. Our contribution is twofold. First, we prove that the analyzing filter bank considered is a frame, and then we define the corresponding dual frame that is necessary for the exact image reconstruction. Second, to deal with the problem of memory overhead, we design a recursive out-of-core blockwise algorithm for the computation of this dual frame. Our work provides a mathematical formalism for the retinal model under study and defines a simple and exact reverse transform for it with over than 265 dB of increase in the peak signal-to-noise ratio quality compared to . Furthermore, the framework presented here can be extended to several models of the visual cortical areas using redundant representations

Another look at the retina as an image dithered scalar quantizer

International audienceWe explore, in this paper, the behavior of the mammalians retina considered as an analog-to-digital converter for the incoming light stimuli. This work extends our previous effort towards combining results in neurosciences with image processing techniques [1]. We base our study on a biologically realistic model that reproduces the neural code as generated by the retina. The neural code, that we consider here, consists of non-deterministic temporal sequences of uniformly shaped electrical impulses, also termed as spikes. We describe, starting from this spike-based code, a dynamic quantization scheme that relies on the so-called rate coding hypothesis. We, then, propose a possible decoding procedure. This yields an original quantizing/de-quantizing system which evolves dynamically from coarse to fine, and from uniform to non-uniform. Furthermore, we emit a possible interpretation for the non-determinism observed in the spike timings. In order to do this, we implement a three-staged processing system mapping the anatomical architecture of the retina. We, then, model the retinal noise by a dither signal which permits us to define the retina behavior as a non-subtractive dithered quantizer. The quantizing/de-quantizing system, that we propose, offers several interesting features as time scalability as well as reconstruction error whitening and de-correlation from the input stimuli

Frames for Exact Inversion of the Rank Order Coder

Our goal is to revisit rank order coding by proposing an original exact decoding procedure for it. Rank order coding was proposed by Simon Thorpe et al. who stated that the retina represents the visual stimulus by the order in which its cells are activated. A classical rank order coder/decoder was then designed on this basis [1]. Though, it appeared that the decoding proce- dure employed yields reconstruction errors that limit the model Rate/Quality performances when used as an image codec. The attempts made in the litera- ture to overcome this issue are time consuming and alter the coding procedure, or are lacking mathematical support and feasibility for standard size images. Here we solve this problem in an original fashion by using the frames theory, where a frame of a vector space designates an extension for the notion of basis. First, we prove that the analyzing filter bank considered is a frame, and then we define the corresponding dual frame that is necessary for the exact image reconstruction. Second, to deal with the problem of memory overhead, we de- sign a recursive out-of-core blockwise algorithm for the computation of this dual frame. Our work provides a mathematical formalism for the retinal model under study and defines a simple and exact reverse transform for it with up to 270 dB of PSNR gain compared to [1]. Furthermore, the framework presented here can be extended to several models of the visual cortical areas using redundant representations.Notre objectif est de revisiter le codage d'images statiques par rang en proposant une procédure originale de décodage exact. Le codage par rang a été proposé par Simon Thorpe et al. qui a affirmé que la rétine représente le stimulus visuel par l'ordre selon lequel ses cellules sont activées. Un codeur par ordre classique ainsi que le décodeur ont ensuite été conçus se basant sur ces résultats [1]. Cependant, il s'avère que la procédure de décodage employé engendre des erreurs de reconstruction qui limitent les performances Débit / Qualité du modèle lorsqu'il est utilisé comme un codec d'images. Les tentatives proposées dans la littérature pour surmonter ce problème prennent du temps et modifie la procédure de codage, ou manquent d'apport mathématique et de faisabilité pour des images de tailles standards. Ici nous résolvons ce problème de façon originale en utilisant la théorie des "frames", où une frame d'un espace vectoriel désigne une extension de la notion de base. Tout d'abord, nous montrons que le banc de filtres d'analyse considéré est une frame, puis nous définissons la frame duale correspondante qui est nécessaire pour la reconstruction exacte de l'image. Deuxièmement, pour faire face au problème du débordement de mémoire, nous concevons un algorithme récursif, out-of-core, et opérant par blocs pour le calcul de cette frame duale. Notre travail fournit un formalisme mathématique pour le modèle de la rétine à l'étude et définit une inversion simple et exacte de la transformée bio-inspirée définie dans [1] avec un maximum de 270 dB de gain de PSNR par rapport au modèle originel. Par ailleurs, le travail présenté ici peut être étendu à plusieurs autres modèles de zones corticales visuelles utilisant des représentations redondantes