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

Data Hiding of Motion Information in Chroma and Luma Samples for Video Compression

International audience2010 appears to be the launching date for new compression activities intended to challenge the current video compression standard H.264/AVC. Several improvements of this standard are already known like competition-based motion vector prediction. However the targeted 50% bitrate saving for equivalent quality is not yet achieved. In this context, this paper proposes to reduce the signaling information resulting from this vector competition, by using data hiding techniques. As data hiding and video compression traditionally have contradictory goals, a study of data hiding is first performed. Then, an efficient way of using data hiding for video compression is proposed. The main idea is to hide the indices into appropriately selected chroma and luma transform coefficients. To minimize the prediction errors, the modification is performed via a rate-distortion optimization. Objective improvements (up to 2.3% bitrate saving) and subjective assessment of chroma loss are reported and analyzed for several sequences

Multiple description video coding for real-time applications using HEVC

Remote control vehicles require the transmission of large amounts of data, and video is one of the most important sources for the driver. To ensure reliable video transmission, the encoded video stream is transmitted simultaneously over multiple channels. However, this solution incurs a high transmission cost due to the wireless channel's unreliable and random bit loss characteristics. To address this issue, it is necessary to use more efficient video encoding methods that can make the video stream robust to noise. In this paper, we propose a low-complexity, low-latency 2-channel Multiple Description Coding (MDC) solution with an adaptive Instantaneous Decoder Refresh (IDR) frame period, which is compatible with the HEVC standard. This method shows better resistance to high packet loss rates with lower complexity

Retinal filtering and image reconstruction

In previous work, we have proposed a bio-plausible model of retinal processing, based on the physiological literature. A strong characteristic of this model was to reproduce the temporal delay of the surround component of filtering, as observed in real retinas. In this report, we study the possibilities of image reconstruction based on the response of such a model to the sudden appearance of a static image. In this report, we mostly focus on the first stage of the model, which performs \emph{linear spatio-temporal filtering} on the input image. We view this stage as a linear application from a 2D space (the image) to a 2D+T space (the temporal response of the retina), which bears an optimal inverse transformation in terms of robustness to noise: the pseudo-inverse of Moore-Penrose. We study the particular structure of this pseudo-inverse, due to the structure of retinal filtering with a delayed surround component. As a result, the pseudo-inverse-based image reconstruction reconstructs low spatial frequencies before high spatial frequencies. This property could have psychophysical correlates, for example during perception of very short image presentations

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

DNA CODING FOR IMAGE STORAGE USING IMAGE COMPRESSION TECHNIQUES

International audienceLiving in the age of the digital media explosion the urge for finding new efficient methods of data storage increases significantly. Existing storage devices such as hard disks, flash, tape or even optical storage have limited durability in the range of 5 to 20 years. Recent studies have proven that the method of DNA data storage introduces a strong candidate to achieve data longevity. The DNA's biological properties permit the compression of a great amount of information into an extraordinary small volume while also promising efficient data storage for millions of years with no loss of information. This work proposes a new encoding scheme especially designed for the encoding of still images, extending the existing algorithms of DNA data storage by introducing image compression techniques

Dynamic Quantization using Spike Generation Mechanisms

This paper introduces a neuro-inspired co-ding/decoding mechanism of a constant real value by using a Spike Generation Mechanism (SGM) and a combination of two Spike Interpretation Mechanisms (SIM). One of the most efficient and widely used SGMs to encode a real value is the Leaky-Integrate and Fire (LIF) model which produces a spike train. The duration of the spike train is bounded by a given time constraint. Seeking for a simple solution of how to interpret the spike train and to reconstruct the input value, we combine two different kinds of SIMs, the time-SIM and the rate-SIM. The time-SIM allows a high quality interpretation of the neural code and the rate-SIM allows a simple decoding mechanism by couting the spikes. The resulting coding/decoding process, called the Dual-SIM Quantizer (Dual-SIMQ), is a non-uniform quantizer. It is shown that it coincides with a uniform scalar quantizer under certain assumptions. Finally, it is also shown that the time constraint can be used to control automatically the reconstruction accuracy of this time-dependent quantizer

Implicit Neural Multiple Description for DNA-based data storage

DNA exhibits remarkable potential as a data storage solution due to its impressive storage density and long-term stability, stemming from its inherent biomolecular structure. However, developing this novel medium comes with its own set of challenges, particularly in addressing errors arising from storage and biological manipulations. These challenges are further conditioned by the structural constraints of DNA sequences and cost considerations. In response to these limitations, we have pioneered a novel compression scheme and a cutting-edge Multiple Description Coding (MDC) technique utilizing neural networks for DNA data storage. Our MDC method introduces an innovative approach to encoding data into DNA, specifically designed to withstand errors effectively. Notably, our new compression scheme overperforms classic image compression methods for DNA-data storage. Furthermore, our approach exhibits superiority over conventional MDC methods reliant on auto-encoders. Its distinctive strengths lie in its ability to bypass the need for extensive model training and its enhanced adaptability for fine-tuning redundancy levels. Experimental results demonstrate that our solution competes favorably with the latest DNA data storage methods in the field, offering superior compression rates and robust noise resilience.Comment: Xavier Pic and Trung Hieu Le are both equal contributors and primary author