Lossless Audio Compression Research Articles

Standard Evolution of 5G-Advanced and Future Mobile Network for Extended Reality and Metaverse

eXtend Reality (XR) and Metaverse are important service scenarios to be supported by 5G-Advanced (5G-A) and future mobile networks. However, there are still many challenges and gaps for 5G-A to guarantee the immersive experience of XR and Metaverse. This article analyzes the challenges to support XR in 5G-A, e.g., lossless audio compression transmission, full-view high-resolution video transmission, and synchronization between multi-modal transmission. Then an overall system architecture of 5G-A for enabling XR is proposed with the concept of application-network coordination. And the latest advances of 3GPP standard on network architecture enhancements for XR are overviewed, including network-assistant service optimization, Quality of Service (QoS) enhancements, and power saving optimization. Finally, the future standard and research directions for Metaverse are analyzed, including integrated architecture with communication-sensing-computing, immersive experience enhancements for new media types, intelligent network, security and privacy aspects.

Improving sign-algorithm convergence rate using natural gradient for lossless audio compression

In lossless audio compression, the predictive residuals must remain sparse when entropy coding is applied. The sign algorithm (SA) is a conventional method for minimizing the magnitudes of residuals; however, this approach yields poor convergence performance compared with the least mean square algorithm. To overcome this convergence performance degradation, we propose novel adaptive algorithms based on a natural gradient: the natural-gradient sign algorithm (NGSA) and normalized NGSA. We also propose an efficient natural-gradient update method based on the AR(p) model, which requires mathcal {O}(p) multiply–add operations at every adaptation step. In experiments conducted using toy and real music data, the proposed algorithms achieve superior convergence performance to the SA. Furthermore, we propose a novel lossless audio codec based on the NGSA, called the natural-gradient autoregressive unlossy audio compressor (NARU), which is open-source and implemented in C. In a comparative experiment with existing, well-known codecs, NARU exhibits superior compression performance. These results suggest that the proposed methods are appropriate for practical applications.

Designing a novel lossless audio compression technique with the help of optimized graph traversal (LACOGT)

Designing a novel lossless audio compression technique with the help of optimized graph traversal (LACOGT)

Developing a Dynamic Cluster Quantization based Lossless Audio Compression (DCQLAC)

In this paper, an approach has been made to produce a compressed audio without losing any information. The proposed scheme is fabricated with the help of dynamic cluster quantization followed by Burrows Wheeler Transform (BWT) and Huffman coding. The encoding algorithm has been designed in two phases, i.e., dynamic cluster selection (of sampled audio) followed by dynamic bit selection for determining quantization level of individual cluster. Quantization level of each cluster is selected dynamically based on mean square quantization error (MSQE). Bit stream is further compressed by applying Burrows Wheeler Transform (BWT) and Huffman code respectively. Experimental results are supported with current state-of-the-art in audio quality analysis (like statistical parameters (compression ratio, space savings, SNR, PSNR) along with other parameters (encoding time, decoding time, Mean Opinion Score (MOS) and entropy) and compared with other existing techniques.

A High Efficiency Multistage Coder for Lossless Audio Compression using OLS+ and CDCCR method

In this paper, the improvement of the cascaded prediction method was presented. Three types of main predictor block with different levels of complexity were compared, including two complex prediction methods with backward adaptation, i.e., extension Active Level Classification Model (ALCM+) and extended Ordinary Least Square (OLS+). Our own approach to implementation of the effective context-dependent constant component removal block is also presented. Additionally, the improved adaptive arithmetic coder with short, medium and long-term adaptation was presented, and the experiment was carried out comparing the results with other known lossless audio coders against which our method obtained the best efficiency.

A Critical Assessment of Advanced Coding Standards for Lossless Audio Compression

A Critical Assessment of Advanced Coding Standards for Lossless Audio Compression

Comparison of Entropy Coding mechanism on IEEE1857.2 Lossless Audio Compression Standard

<p>This paper has two objectives. First, we aim to review and analyze the performance of the IEEE 1857.2 standard, focusing is on the Golomb-Rice and Arithmetic entropy algorithms as well as the effect of the pre-processing block on these entropy blocks. The pre-processing block normalizes the error residue of the Linear Predictive encoder, which then is passed to Entropy block, where the selector chooses the entropy encoder to use. The second objective is to present results from experimenting different existing algorithms available to benchmark it’. The results are discussed, and comparisons are made to identify the effect on compression ratio and encoding speed of the lossless encoder. As well as this, comparison is made to analyze effects of enabling and disabling the pre-processing output to the Entropy Coding block. We concluded that pre-processing block works well to flatten the output at lower predictor order for all the sound types, but works best at improving the residual output for music sound type.</p>

Investigation of Lossless Audio Compression using IEEE 1857.2 Advanced Audio Coding

<p>Audio compression is a method of reducing the space demand and aid transmission of the source file which then can be categorized by lossy and lossless compression. Lossless audio compression was considered to be a luxury previously due to the limited storage space. However, as storage technology progresses, lossless audio files can be seen as the only plausible choice for those seeking the ultimate audio quality experience. There are a lot of commonly used lossless codecs are FLAC, Wavpack, ALAC, Monkey Audio, True Audio, etc. The IEEE Standard for Advanced Audio Coding (IEEE 1857.2) is a new standard approved by IEEE in 2013 that covers both lossy and lossless audio compression tools. A lot of research has been done on this standard, but this paper will focus more on whether the IEEE 1857.2 lossless audio codec to be a viable alternative to other existing codecs in its current state. Therefore, the objective of this paper is to investigate the codec’s operation as initial measurements performed by researchers show that the lossless compression performance of the IEEE compressor is better than any traditional encoders, while the encoding speed is slower which can be further optimized.</p>

Advanced Audio Compression For Lossless Audio Coding Using Ieee 1857.2

Developed by IEEE-SA (Standard Association) and released in August 2013, IEEE 1857.2 is the latest standard for lossless audio compression. This recent standard for advanced audio coding (AAC) is an efficient lossless audio codec (coding decoding) technique, in improving audio quality for compression and decompression, optimizing bandwidth during transmission, saving storage space, it also speeds up the video and audio streaming, and others video and audio data. It defines a set of tools to support specific audio coding functions, including general audio coding and lossless coding. This project presents the algorithms for lossless audio coding communication, the higher efficiency of this standard and other assets such as compression ratio, encoding and decoding speed of this standard. Different types of audio files have compressed and the results show a good compression ratio (CR) which depends on audio file type, and good encoding and decoding speed.

New Ieee Standard For Advanced Audio Coding In Lossless Audio Compression : A Literature Review

Due to non-stop increase of communication subscribers, the loss and distortion of data (voice, video...) is emphasized. Meanwhile, IEEE-SA (Standard Association) has developed a new standard for advanced audio coding (AAC) in August 2013, called IEEE 1857.2 for contributing to those issues. This new standard for AAC is an efficient lossless audio codec (coding decoding) technique, in improving audio quality for compression and decompression, optimizing bandwidth during transmission, saving storage space, speeding up the video streaming, audio streaming and others video or audio data. This survey consists not only of describing the different existing techniques for lossless audio compression, and also of showing the state of the art of this recent lossless standard for AAC upon its most popular predecessors.

Sparse Modeling for Lossless Audio Compression

We investigate the problem of sparse modeling for predictive coding and introduce an efficient algorithm for computing sparse stereo linear predictors for lossless audio compression. Sparse linear predictive coding offers both improved compression and reduction of decoding complexity compared with non-sparse linear predictive coding. The modeling part amounts to finding the optimal structure of a sparse linear predictor using a fully implementable minimum description length (MDL) approach. The MDL criterion, simplified conveniently under realistic assumptions, is approximately minimized by a greedy algorithm which solves sequentially least squares partial problems, where the <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LDL</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</sup> factorization ensures numerically stable solutions and facilitates a quasi-optimal quantization of the parameter vector. The overall compression system built around this modeling tool is shown to achieve the main goals: improved compression and, even more importantly, faster decoding speeds than the state of the art lossless audio compression methods. The optimal MDL sparse predictors are shown to provide parametric spectra that constitute new alternative spectral descriptors, capturing important regularities missed by the optimal MDL non-sparse predictors.

Highly scalable IP core to accelerate the forward/backward modified discrete cosine transform in MP3 implemented to FPGA and low-power ASIC

Modified discrete cosine transform (MDCT) is used in many audio coding standards for time-to-frequency transformation of digital signals. It is one of the most computationally intensive operations in audio compression and decompression processes. In this study, optimised dedicated hardware architectures utilised in a highly scalable MDCT IP core are proposed to accelerate the forward/backward MDCT computation in MP3 audio coding standard. The MDCT IP core is pipelined, capable to compute both the forward and backward MDCT on the same hardware and it is optimised with field-programmable gate arrays (FPGA) and application-specific integrated circuit (ASIC) technologies. The MDCT IP core is implemented to FPGA and ASIC, whereby the FPGA implementation used Xilinx Virtex-4 FPGA, while the ASIC implementation used AMS 350 nm CMOS standard cell library. The MDCT IP core is further optimised and implemented utilising UMC 90 nm CMOS low-power digital libraries and clock gating technique. As a result, power consumption and the area are reduced significantly. The proposed hardware architectures are optimised to achieve high computational speed with high precision, and therefore they are suitable for a lossless audio compression. In particular, high computational speed permits multichannel real-time acceleration of the forward and backward MDCT computation.

Lossless compression of digital audio

Lossless audio compression is likely to play an important part in music distribution over the Internet, DVD audio, digital audio archiving, and mixing. The article is a survey and a classification of the current state-of-the-art lossless audio compression algorithms. This study finds that lossless audio coders have reached a limit in what can be achieved for lossless compression of audio. It also describes a new lossless audio coder called AudioPak, which low algorithmic complexity and performs well or even better than most of the lossless audio coders that have been described in the literature.

Adaptive context-based sequential prediction for lossless audio compression

In this paper we propose the use of adaptive context-based prediction in a sequential mode for lossless audio compression. We show that lossless compression algorithms with sequential context-based prediction can achieve better compression results than with forward-frame-based linear prediction. The proposed coding scheme uses parametric modelling of errors in a large number of contexts in conjunction with Golomb–Rice encoding. Context quantization and prediction are similar to those introduced in an algorithm previously proposed for image compression (Tăbuş et al., Proceedings of ICIP’97 International Conference on Image Processing, Santa Barbara, California, October 1997, pp. 401–404) but new solutions are provided to cope with the wide dynamical range of the prediction error and, optionally, to exploit the long-time dependencies, very common in audio or speech signals. The experimental results show the good performance of the proposed technique for audio signals sampled at 48 kHz with 16 bits/sample while the variant including long-time prediction is shown to perform very well for speech signal sampled at 8 kHz with 16 bits/sample.

Audio compression based on sine‐crossing representations of signals

The sampling theorem of Bar‐David [J. Bar‐David, Info. Control 24, 36–44 (1974)] provides an implicit representation of bandlimited signals using their crossings with a sine function. This sine function is chosen in a way that guarantees a unique representation of the signal. On a finite signal, this leads to a multiplicative representation involving a Riesz product whose roots form the representation of the original signal. These roots can be encoded to provide high‐quality, near lossless audio compression. The sine crossing representation can also be combined with the wavelet transform to provide further compression. [Work supported by ONR.]