Robust VLC sequence decoding exploiting additional video stream properties with reduced complexity

Joint source-channel decoding of variable length codes for image and video streaming transmission over unreliable links, such as wireless networks, is a subject of increasing interest. We first provide an analysis of available redundancy in compressed video data produced by some commonly used standardized codecs. Then, we show that a significant improvement in decoding performance can be achieved when additional image and video data properties are taken into consideration. Finally, performance results for improving video decoding over wireless channels are presented.

A concurrent variable length coding (VLC) decoder, introduced in Hsieh and Kim (1996) and Shaw-Min Lei and Ming-Ting Sun (1989), can achieve high system performance by decoding VLC codes concurrently by exploiting the principle of variable-length coding statistical characteristics, shorter codewords appear often. For concurrent VLC decoding, the better the decoding throughput, the more complicated the hardware and control. Therefore, a compromise should be made between performance and cost. In this paper, a systematic approach based on mathematical models for a concurrent VLC decoder is proposed. The tradeoff between system performance and hardware cost is investigated.

Fixed length entropy codes (FLC) have been employed as an alternative to the usual variable length codes (VLC) in video codecs. The effect of a transmission error in an FLC coded stream does not propagate beyond the corrupted codeword because of its fixed parsing structure. This paper presents a new decoding algorithm for FLC compressed video streams that fully exploits the error resilience properties of FLC codes combined with spatial synchronization points. While maintaining the compression efficiency of VLC coded video streams, the proposed solution is a novel idea to combat the consequences of transmission errors and it is totally compatible with more traditional solutions such as error concealment and forward error correcting codes.

Joint source and channel decoding (JSCD) has been proved to be an effective technique which can improve decoding performance by exploiting residual source redundancy. Most previous publications on this subject focus on a traditional coding scheme in which the source variable-length coding (VLC) is serially concatenated with a channel code. In this paper, a parallel concatenated coding scheme for the VLC combined with a turbo code is presented. By merging a symbol-level VLC trellis with a convolutional trellis, we construct a symbol-level joint trellis with compound states. Also, a solution of the symbol-by-symbol a posteriori probability (APP) decoding algorithm based on this joint trellis is derived, which leads to an iterative JSCD approach in the similar way to the classical turbo decoder. The simulation results show that our joint source-channel en/decoding system achieves some gains at the cost of increasing decoding complexity, when compared to the joint iterative decoding based on the bit-level super trellis for the separate coding system.

Successive versions of the MPEG and JPEG coding standards have been widely adopted for compression of moving pictures and still images. Variable-length codes (VLC) such as Huffman codes are generally employed within the frameworks of these standards to achieve optimal coding efficiency in the entropy coding stage. Conventional table look-up encoding/decoding techniques for VLC are inefficient in memory usage. This is a particularly important consideration for hand-held devices, such as cellular phones with image reception and processing capabilities. The authors present a series of efficient and simple algorithms for VLC encoding and decoding based on a pointer look-up approach. They present both software and hardware implementations of these algorithms, and a comparison of memory requirements associated with the pointer look-up and conventional techniques.

Variable length codes (VLCs), used in data compression, are very sensitive to error propagation in the presence of noisy channels. Addressing this problem with joint source-channel turbo techniques has been proposed in the literature and looks quite promising. But to date, most code-related conclusions are based on simulations. This paper states and proves several theoretical results about the robustness of prefix VLCs concatenated with linear error correcting codes (ECC), assuming a maximum likelihood decoder. Especially, an approximate and asymptotically tight distance spectrum of the concatenated code (VLC+ECC) is rigorously developed. Together with the union bound, it provides upper bounds on the symbol and frame/packet error rates.

Joint source and channel decoding of compressed still images

In this paper, we propose a near maximum likelihood (ML) decoding technique, which reduces the computational complexity of the exact ML decoding algorithm. The computations needed for the tree search in the ML decoding is simplified by reducing the dimension of the search space prior to the tree search. In order to compensate performance loss due to the dimension reduction, a list stack algorithm (LSA) is considered, which produces a list of the top K closest points. The combination of both approaches, called reduced dimension list stack algorithm (RD-LSA), is shown to provide flexibility and offers a performance-complexity trade-off. Simulations performed for V-BLAST transmission demonstrate that significant complexity reduction can be achieved compared to the sphere decoding algorithm (SDA) while keeping the performance loss below an acceptable level.

In general, context-based adaptive variable length coding (CAVLC) decoding in H.264/AVC standard requires frequent access to the unstructured variable length coding tables (VLCTs) and significant memory accesses are consumed. Heavy memory accesses will cause high power consumption and time delays, which are serious problems for applications in portable multimedia devices. We propose a method for high-efficiency CAVLC decoding by using a program instead of all the VLCTs. The decoded codeword from VLCTs can be obtained without any table look-up and memory access. The experimental results show that the proposed algorithm achieves 100% memory access saving and 40% decoding time saving without degrading video quality. Additionally, the proposed algorithm shows a better performance compared with conventional CAVLC decoding, such as table look-up by sequential search, table look-up by binary search, Moon’s method, and Kim’s method.

This letter considers the average complexity of maximum-likelihood (ML) decoding of convolutional codes. ML decoding can be modeled as finding the most probable path taken through a Markov graph. Integrated with the Viterbi algorithm (VA), complexity reduction methods often use the sum log likelihood (SLL) of a Markov path as a bound to disprove the optimality of other Markov path sets and to consequently avoid exhaustive path search. In this letter, it is shown that SLL-based optimality tests are inefficient if one fixes the coding memory and takes the codeword length to infinity. Alternatively, optimality of a source symbol at a given time index can be testified using bounds derived from log likelihoods of the neighboring symbols. It is demonstrated that such neighboring log likelihood (NLL)-based optimality tests, whose efficiency does not depend on the codeword length, can bring significant complexity reduction. The results are generalized to ML sequence detection in a class of discrete-time hidden Markov systems.

We are motivated by many applications such as problems that arise in online marketing applications, where the observations are governed by non-homogeneous Poisson models. We analyze the performance of a Maximum Likelihood (ML) decoder. We prove consistency and show an exponential rate of converge for sparse recovery in the high-dimensional Poisson setting. After verifying the efficiency of ML estimator empirically, we apply the ML decoder to study the dynamics of online marketing methods over time.

Nonreversible variable-length codes (e.g. Huffman coding, Golomb-Rice coding, and arithmetic coding) have been used in source coding to achieve efficient compression. However, a single bit error during noisy transmission can cause many codewords to be misinterpreted by the decoder. In recent years, increasing attention has been given to the design of reversible variable-length codes (RVLCs) for better data transmission in error-prone environments. RVLCs allow instantaneous decoding in both directions, which affords better detection of bit errors due to synchronization losses over a noisy channel. RVLCs have been adopted in emerging video coding standards--H.263+ and MPEG-4--to enhance their error-resilience capabilities. Given the large volume of three-dimensional data that will be generated by future space-borne ultraspectral sounders (e.g. IASI, CrIS, and HES), the use of error-robust data compression techniques will be beneficial to satellite data transmission. In this paper, we investigate a reversible variable-length code for ultraspectral sounder data compression, and present its numerical experiments on error propagation for the ultraspectral sounder data. The results show that the RVLC performs significantly better error containment than JPEG2000 Part 2.

This paper proposes an efficient pruning algorithm for MIMO detector, which not only achieves the same performance of the maximum likelihood (ML) decoder, but also massively reduces the number of visiting nodes. The proposed algorithm applies correct sorting order so that it achieves the same performance of the ML decoder. Thanks to the use of the correct ordering, both child and sibling nodes can be pruned efficiently. As a result, the number of visiting nodes is significantly reduced. To acquire properly sorted order, we propose fast sorting algorithms that generate the exact sorted sequence. Furthermore, the proposed schemes can be applied in SISO MIMO detector. The advantages of the proposed sphere decoder become more pronounced in high-order modulation schemes.

SUMMARY This paper presents an improved version of multistage threshold decoding with a difference register (MTD-DR) for selforthogonal convolutional codes (SOCCs). An approximate lower bound on the bit error rate (BER) with the maximum likelihood (ML) decoding is also given. MTD-DR is shown to achieve an approximate lower bound of ML decoding performance at the higher Eb/N0. The code with larger minimum Hamming distance reduces the BER in error floor, but the BER in waterfall shifts to the higher Eb/N0. This paper gives a decoding scheme that improves the BER in both directions, waterfall and error floor. In the waterfall region, a 2-step decoding (2SD) improves the coding gain of 0.40 dB for shorter codes (code length 4200) and of 0.55 dB for longer codes (code length 80000) compared to the conventional MTD-DR. The 2-step decoding that serially concatenates the parity check (PC) decoding improves the BER in the error floor region. This paper gives an effective use of PC decoding, that further makes the BER 1/8 times compared to the ordinary use of PC decoding in the error floor region. Therefore, the 2SD with effective use of parity check decoding improves the BER in the waterfall and the error floor regions simultaneously.

We consider the topic of universal decoding with a decoder that does not have direct access to the codebook, but only to noisy versions of the various randomly generated codewords, a problem motivated by biometrical identification systems. Both the source that generates the original (clean) codewords, and the channel that corrupts them in generating the noisy codewords, as well as the main channel for communicating the messages, are all modeled by non–unifilar, finite–state systems (hidden Markov models). As in previous works on universal decoding, here too, the average error probability of our proposed universal decoder is shown to be as small as that of the optimal maximum likelihood (ML) decoder, up to a multiplicative factor that is a sub–exponential function of the block length. It therefore has the same error exponent, whenever the ML decoder has a positive error exponent. The universal decoding metric is based on Lempel–Ziv incremental parsing of each noisy codeword jointly with the given channel output vector, but this metric is somewhat different from the one proposed in earlier works on universal decoding for finite–state channels, by Ziv (1985) and by Lapidoth and Ziv (1998). The reason for the difference is that here, unlike in those earlier works, the probability distribution that governs the (noisy) codewords is, in general, not uniform across its support. This non–uniformity of the codeword distribution also makes our derivation more challenging. Another reason for the more challenging analysis is the fact that the effective induced channel between the noisy codeword of the transmitted message and the main channel output is not a finite–state channel in general.