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

A VLSI architecture of JPEG2000 encoder

Copyright @ 2004 IEEEThis paper proposes a VLSI architecture of JPEG2000 encoder, which functionally consists of two parts: discrete wavelet transform (DWT) and embedded block coding with optimized truncation (EBCOT). For DWT, a spatial combinative lifting algorithm (SCLA)-based scheme with both 5/3 reversible and 9/7 irreversible filters is adopted to reduce 50% and 42% multiplication computations, respectively, compared with the conventional lifting-based implementation (LBI). For EBCOT, a dynamic memory control (DMC) strategy of Tier-1 encoding is adopted to reduce 60% scale of the on-chip wavelet coefficient storage and a subband parallel-processing method is employed to speed up the EBCOT context formation (CF) process; an architecture of Tier-2 encoding is presented to reduce the scale of on-chip bitstream buffering from full-tile size down to three-code-block size and considerably eliminate the iterations of the rate-distortion (RD) truncation.This work was supported in part by the China National High Technologies Research Program (863) under Grant 2002AA1Z142

Evaluation of GPU/CPU Co-Processing Models for JPEG 2000 Packetization

With the bottom-line goal of increasing the throughput of a GPU-accelerated JPEG 2000 encoder, this paper evaluates whether the post-compression rate control and packetization routines should be carried out on the CPU or on the GPU. Three co-processing models that differ in how the workload is split among the CPU and GPU are introduced. Both routines are discussed and algorithms for executing them in parallel are presented. Experimental results for compressing a detail-rich UHD sequence to 4 bits/sample indicate speed-ups of 200x for the rate control and 100x for the packetization compared to the single-threaded implementation in the commercial Kakadu library. These two routines executed on the CPU take 4x as long as all remaining coding steps on the GPU and therefore present a bottleneck. Even if the CPU bottleneck could be avoided with multi-threading, it is still beneficial to execute all coding steps on the GPU as this minimizes the required device-to-host transfer and thereby speeds up the critical path from 17.2 fps to 19.5 fps for 4 bits/sample and to 22.4 fps for 0.16 bits/sample

Bitplane image coding with parallel coefficient processing

Image coding systems have been traditionally tailored for multiple instruction, multiple data (MIMD) computing. In general, they partition the (transformed) image in codeblocks that can be coded in the cores of MIMD-based processors. Each core executes a sequential flow of instructions to process the coefficients in the codeblock, independently and asynchronously from the others cores. Bitplane coding is a common strategy to code such data. Most of its mechanisms require sequential processing of the coefficients. The last years have seen the upraising of processing accelerators with enhanced computational performance and power efficiency whose architecture is mainly based on the single instruction, multiple data (SIMD) principle. SIMD computing refers to the execution of the same instruction to multiple data in a lockstep synchronous way. Unfortunately, current bitplane coding strategies cannot fully profit from such processors due to inherently sequential coding task. This paper presents bitplane image coding with parallel coefficient (BPC-PaCo) processing, a coding method that can process many coefficients within a codeblock in parallel and synchronously. To this end, the scanning order, the context formation, the probability model, and the arithmetic coder of the coding engine have been re-formulated. The experimental results suggest that the penalization in coding performance of BPC-PaCo with respect to the traditional strategies is almost negligible

深層学習に基づく画像圧縮と品質評価

早大学位記番号:新8427早稲田大

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