Amin Zribi

Low-Complexity Soft Decoding of Huffman Codes and Iterative Joint Source Channel Decoding

Most source coding standards (voice, audio, image and video) use Variable-Length Codes (VLCs) for compression. However, the VLC decoder is very sensitive to transmission errors in the compressed bit-stream. Previous contributions, using a trellis description of the VLC codewords to perform soft decoding, have been proposed. Significant improvements are achieved by this approach when compared with prefix decoding. Nevertheless, for realistic VLCs, the complexity of the trellis technique becomes intractable. In this paper, we propose a soft-input VLC decoding method using an a priori knowledge of the lengths of the source-symbol sequence and the compressed bit-stream with Maximum A Posteriori (MAP) sequence estimation. Performance in the case of transmission over an Additive White Gaussian Noise (AWGN) channel is evaluated. Simulation results show that the proposed decoding algorithm leads to significant performance gain in comparison with the prefix VLC decoding besides exhibiting very low complexity. A new VLC decoding method generating additional information regarding the reliability of the bits of the compressed bit-stream is also proposed. We consider the serial concatenation of a VLC with two types of channel code and perform iterative decoding. Results show that, when concatenated with a recursive systematic convolutional code (RSCC), iterative decoding provides remarkable error correction performance. In fact, a gain of about 2.3 dB is achieved, in the case of transmission over an AWGN channel, with respect to tandem decoding. Second, we consider a concatenation with a low-density parity-check (LDPC) code and it is shown that iterative joint source/channel decoding outperforms tandem decoding and an additional coding gain of 0.25 dB is achieved.

Joint source/channel iterative arithmetic decoding with JPEG 2000 image transmission application

Motivated by recent results in Joint Source/Channel coding and decoding, we consider the decoding problem of Arithmetic Codes (AC). In fact, in this article we provide different approaches which allow one to unify the arithmetic decoding and error correction tasks. A novel length-constrained arithmetic decoding algorithm based on Maximum A Posteriori sequence estimation is proposed. The latter is based on soft-input decoding using a priori knowledge of the source-symbol sequence and the compressed bit-stream lengths. Performance in the case of transmission over an Additive White Gaussian Noise channel is evaluated in terms of Packet Error Rate. Simulation results show that the proposed decoding algorithm leads to significant performance gain while exhibiting very low complexity. The proposed soft input arithmetic decoder can also generate additional information regarding the reliability of the compressed bit-stream components. We consider the serial concatenation of the AC with a Recursive Systematic Convolutional Code, and perform iterative decoding. We show that, compared to tandem and to trellis-based Soft-Input Soft-Output decoding schemes, the proposed decoder exhibits the best performance/complexity tradeoff. Finally, the practical relevance of the presented iterative decoding system is validated under an image transmission scheme based on the JPEG 2000 standard and excellent results in terms of decoded image quality are obtained.

Low-Complexity Joint Source/Channel Turbo Decoding of Arithmetic Codes with Image Transmission Application

In this paper a novel joint source channel (JSC) decoding technique is presented. The proposed approach enables iterative decoding for serially concatenated arithmetic codes and convolutional codes. Iterations are performed between Soft In Soft Out (SISO) component decoders. For arithmetic decoding, we proposed to employ a low complex trellis search technique to estimate the best transmitted codewords and generate soft outputs. Performance of the presented system are evaluated in terms of PER, in the case of transmission across the AWGN channel. Simulation results show that the proposed JSC iterative scheme leads to significant gain in comparison with a traditional separated decoding. Finally, the practical relevance of the proposed technique is validated under an image transmission system using the SPIHT codec.

Low-complexity joint source/channel turbo decoding of arithmetic codes

In recent years, the turbo principle has been widely applied to various decoding schemes. Here, we extend the general idea of iterative decoding to a communication scheme where arithmetic coding is used for source coding and a recursive systematic convolutional code is used for channel coding. The scope of this paper is to develop a low complexity SISO decoder for arithmetic codes using the SOVA algorithm principle and a trellis representation of arithmetic codes. The proposed approach enables iterative decoding and information exchange between the convolutional SISO decoder and the proposed arithmetic SISO decoder. Performance of the presented scheme are evaluated for an artificial source transmitting over an AWGN channel. Simulation results show that the proposed joint source channel iterative decoder leads to a significant performance gain in comparison with classical tandem decoding.

Soft-input arithmetic decoding for optimized scalable image transmission over a realistic MIMO channel

In this paper, we investigate a new communication strategy for embedded image transmission over a realistic Multiple-Input Multiple-Output (MIMO) wireless channel. The objective of our approach is to guarantee Quality of Service (QoS) required by the user for all possible channel states. In this scope, two major improvements are considered. The first is based on Unequal Power Allocation (UPA) and aims at optimizing the transmit power over the different antennas in order to maximize the image quality at the reception. The second contribution is based on Joint Source-Channel (JSC) soft-input arithmetic decoding and helps to decrease the error rates at the reception without including extra redundancy. We considered Robust SPIHT (R-SPIHT) source encoder to generate an embedded bit-stream. Simulation results illustrate good image quality improvements at the receiver side, with significant Peak Signal to Noise Ratio (PSNR) gains, especially for a realistic noisy channel provided by a 3D ray-tracing software.

Chase-Like Decoding of Arithmetic Codes with Image Transmission Applications

Several recent contributions have demonstrated that Joint Source/ Channel (JSC) decoding could be a powerful technique to make error correction in the case of transmission of entropy encoded streams. This paper addresses a new scheme for JSC decoding of Arithmetic Codes (AC) based on Maximum A Posteriori (MAP) sequence estimation. The proposed algorithm performs Chase-like decoding using a priori knowledge of the source symbol sequence and the compressed bit stream lengths. Simulation results show that the proposed decoding algorithm leads to significant performance gain in comparison with classical arithmetic decoding, while exhibiting very low complexity. The practical relevance of the proposed technique is validated in the case of image transmission across the AWGN channel. Lossless and lossy image compression schemes were considered, and the Chase-like arithmetic decoder shows excellent results.

Graph-based Joint Source Channel LDPC decoding for cooperative communication with error-corrupted relay observations

In this paper, we design a unified framework decoder for relay systems with iterative decoding. In the proposed scheme, the source node needs to transmit a correlated content to a destination with the help of a relay. Then, a distributed Joint Source Channel (JSC) Low-Density-Parity-Check (LDPC) encoding is applied at the source and the relay. The destination receives simultaneously the source compressed data from the source node, and the source/channel encoded data from the relay node. The cooperative network is mapped into a factor graph on which message passing iterative decoding is applied to estimate the source information. The JSC decoder takes into account the source-relay correlation which involves remarkable improvements even if errors occur at the source-relay link. The Bit Error-Rate (BER) system performance are investigated for different scenario, according to the relay position, and it is shown that the performance of the proposed cooperative scheme is typically about 0.5–1.0 dB better than an equivalent rate point-to-point system.

Erasure coding for reliable adaptive retransmission in wireless broadcast/multicast systems

In this paper, we present new adaptive automatic repeat request (ARQ) schemes for wireless broadcast/multicast combining erasure coding (EC) and packet retransmission. Traditional approaches rely on retransmitting the lost packets in a point-to-point or point-to-multipoint mode. The main idea behind the presented protocols is to retransmit adaptive combinations of the lost packets using EC, which can help several receivers to recover the lost information with fewer retransmission attempts. We propose two versions of EC-based ARQ protocols, and investigate theoretically the corresponding transmission bandwidths in different contexts. We show through simulation results the efficiency of the proposed protocols with respect to conventional ARQ strategies and new published ARQ works for broadcast/multicast. Finally, a new sliding window NACK feedback policy is presented for the case of a high number of receivers to avoid the feedback implosion problem.

Optimized Scalable Image and Video Transmission for MIMO Wireless Channels

In this chapter, we focus on proposing new strategies to efficiently transfer a compressed image/video content through wireless links using a multiple antenna technology. The proposed solutions can be considered as application layer physical layer (APP-PHY) cross layer design methods as they involve optimizing both application and physical layers. After a wide state-of-the-art study, we present two main solutions. The first focuses on using a new precoding algorithm that takes into account the image/video content structure when assigning transmission powers. We showed that its results are better than the existing conventional precoders. Second, a link adaptation process is integrated to efficiently assign coding parameters as a function of the channel state. Simulations over a realistic channel environment show that the link adaptation activates a dynamic process that results in a good image/video reconstruction quality even if the channel is varying. Finally, we incorporated soft decoding algorithms at the receiver side, and we showed that they could induce further improvements. In fact, almost 5 dB peak signal-to-noise ratio (PSNR) improvements are demonstrated in the case of transmission over a Rayleigh channel.