We exploit the redundancy of the language-based source to help polar decoding. By judging the validity of decoded words in the decoded sequence with the help of a dictionary, the polar list decoder constantly detects erroneous paths after every few bits are decoded. This path-pruning technique based on joint decoding has advantages over stand-alone polar list decoding in that most decoding errors in early stages are corrected. In order to facilitate the joint decoding, we first propose a construction of dynamic dictionary using a trie and show an efficient way to trace the dictionary during decoding. Then we propose a joint decoding scheme of polar codes taking into account both information from the channel and the source. The proposed scheme has the same decoding complexity as the list decoding of polar codes. A list-size adaptive joint decoding is further implemented to largely reduce the decoding complexity. We conclude by simulation that the joint decoding schemes outperform stand-alone polar codes with CRC-aided successive cancellation list decoding by over 0.6 dB.

Joint Source-Channel Decoding of Polar Codes for Language-Based Source

The persistent storage of big data requires advanced error correction schemes. The classical approach is to use error correcting codes (ECCs). This work studies an alternative approach, which uses the redundancy inherent in data itself for error correction. This type of redundancy, called Natural Redundancy (NR), is abundant in many types of uncompressed or even compressed files. The complex structures of Natural Redundancy, however, require machine learning techniques. In this paper, we study two fundamental approaches to use Natural Redundancy for error correction. The first approach, called Representation-Oblivious, requires no prior knowledge on how data are represented or compressed in files. It uses deep learning to detect file types accurately, and then mine Natural Redundancy for soft decoding. The second approach, called Representation-Aware, assumes that such knowledge is known and uses it for error correction. Furthermore, both approaches combine the decoding based on NR and ECCs. Both experimental results and analysis show that such an integrated scheme can substantially improve the error correction performance.

Machine Learning for Error Correction with Natural Redundancy

Differential pulse code modulation (DPCM) is able to code highly correlated sources efficiently at high bit rates but not at low bit-rate regions. Motivated by the rate-distortion theory, a simple modified DPCM codec using multirate processing is proposed. A low-pass filter is used to limit the source signal spectrum according to the optimal mapping scheme. Bit rate reduction is achieved by reducing the number of transmitted samples through downsampling. A Wiener filter is appended at the decoder for further noise reduction. Simulation results agree with theoretical analysis and show that for a first-order autoregressive [AR(1)] process, the proposed codec makes substantial improvements over the classic DPCM and performs close to the rate-distortion bound. The proposed system is then implemented as a low-rate predictive image coder. In addition to apply two-dimensional DPCM, adaptive entropy coding is used to exploit variation in the local statistics of the prediction error image. Simulation results show that the system is able to outperform JPEG at low rates and has subjective quality comparable to that of JPEG2000.

Improving the Rate-Distortion Performance of DPCM Using Multirate Processing With Application in Low-Rate Image Coding

For the storage of big data, there are significant challenges with its long-term reliability. This paper studies how to use the natural redundancy in data for error correction, and how to combine it with error-correcting codes to effectively improve data reliability. It explores several aspects of natural redundancy, including the discovery of natural redundancy in compressed data, the efficient decoding of codes with random structures, the capacity of error-correcting codes that contain natural redundancy, and the time-complexity tradeoff between source coding and channel coding.

Correcting errors by natural redundancy

Many types of data, such as natural languages, have rich internal redundancy, which can be used to substantially improve the error correction performance. This work studies machine learning and algorithmic techniques for correcting errors in compressed or uncompressed languages.

Machine Learning and Algorithmic Techniques for Error Correction

A joint source channel coding scheme for error resilient image transmission is proposed. A practical image coder was introduced in AN Kim et al, (2004) using modified differential pulse coded modulation (DPCM) codec with multi-rate processing and adaptive entropy coding. In this paper the residual redundancy of the prediction error image is exploited by using turbo codes for both data compression and error protection. In the paper we deal with robust transmission of the source over a BSC channel, but the results can be easily extended for non binary channels. Note also that simple modification of the quantizer allows for progressive transmission and successive refinement of information. With properly chosen rate and puncturing, the system is able to approach the limit theoretically attainable and to outperform the separated approach that consists on the concatenation of the system in AN Kim et al, (2004) and the best turbo codes for the same spectral efficiency.

Combined error protection and compression using turbo codes for error resilient image transmission

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Combined error protection and compression using turbo codes for error resilient image transmission