Optimizing Apple Lossless Audio Codec Algorithm using NVIDIA CUDA Architecture

Ahmed, Rafid; Islam, Md. Sazzadul; Uddin, Jia

doi:10.11591/ijece.v8i1.pp70-75

Cited by 6 publications

(4 citation statements)

References 11 publications

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“…In Ref. [14] a parallel implementation of the audio compression algorithm Apple Lossless Audio Codec (ALAC) is presented and a speedup of 80-90% is achieved. Heidari et al [15] proposed a parallel implementation of color moments and texture feature and achieved a speed up of 144x over the sequential implementation.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Implementation and Performance Optimization of Local Derivative Pattern Algorithm on GPUs

Chandran

Gangodkar

Mittal

2018

IJECE

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Pattern based texture descriptors are widely used in Content Based Image Retrieval (CBIR) for efficient retrieval of matching images. Local Derivative Pattern (LDP), a higher order local pattern operator, originally proposed for face recognition, encodes the distinctive spatial relationships contained in a local region of an image as the feature vector. LDP efficiently extracts finer details and provides efficient retrieval however, it was proposed for images of limited resolution. Over the period of time the development in the digital image sensors had paid way for capturing images at a very high resolution. LDP algorithm though very efficient in content-based image retrieval did not scale well when capturing features from such high-resolution images as it becomes computationally very expensive. This paper proposes how to efficiently extract parallelism from the LDP algorithm and strategies for optimally implementing it by exploiting some inherent General-Purpose Graphics Processing Unit (GPGPU) characteristics. By optimally configuring the GPGPU kernels, image retrieval was performed at a much faster rate. The LDP algorithm was ported on to Compute Unified Device Architecture (CUDA) supported GPGPU and a maximum speed up of around 240x was achieved as compared to its sequential counterpart.

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Section: Related Workmentioning

confidence: 99%

Real-Time Implementation and Performance Optimization of Local Derivative Pattern Algorithm on GPUs

Chandran

Gangodkar

Mittal

2018

IJECE

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“…The big data era, with the huge amount of data being generated each year, inspires new business lines including cloud service and streaming platforms. This motivates the industry to develop more efficient and effective lossless compression methods (Alakuijala et al 2019;Sneyers and Wuille 2016;Collet and Turner 2016;Ahmed, Islam, and Uddin 2018). According to Shannon's source coding theorem, the more accurately the distribution of the data can be estimated, the better the limits of compression can be reached (MacKay 2003).…”

Section: Introductionmentioning

confidence: 99%

DAMix: Exploiting Deep Autoregressive Model Zoo for Improving Lossless Compression Generalization

Dong

Zhou

Kang

et al. 2023

AAAI

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Deep generative models have demonstrated superior performance in lossless compression on identically distributed data. However, in real-world scenarios, data to be compressed are of various distributions and usually cannot be known in advance. Thus, commercially expected neural compression must have strong Out-of-Distribution (OoD) generalization capabilities. Compared with traditional compression methods, deep learning methods have intrinsic flaws for OoD generalization. In this work, we make the attempt to tackle this challenge via exploiting a zoo of Deep Autoregressive models (DAMix). We build a model zoo consisting of autoregressive models trained on data from diverse distributions. In the test phase, we select useful expert models by a simple model evaluation score and adaptively aggregate the predictions of selected models. By assuming the outputs from each expert model are biased in favor of their training distributions, a von Mises-Fisher based filter is proposed to recover the value of unbiased predictions that provides more accurate density estimations than a single model. We derive the posterior of unbiased predictions as well as concentration parameters in the filter, and a novel temporal Stein variational gradient descent for sequential data is proposed to adaptively update the posterior distributions. We evaluate DAMix on 22 image datasets, including in-distribution and OoD data, and demonstrate that making use of unbiased predictions has up to 45.6% improvement over the single model trained on ImageNet.

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“…The development of digital multimedia and communication applications and the huge volume of data transfer through the internet place the necessity for data compression methods at the first priority [1]. The main purpose of data compression is to reduce the amount of transferred data by eliminating the redundant information to keep the transmission bandwidth and without significant effects on the quality of the data [2]. In addition, the compression time should be minimized.…”

Section: Introductionmentioning

confidence: 99%

Image and audio fractal compression: comprehensive review, enhancements and research directions

Ali

Abbas²,

George

et al. 2019

IJEECS

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This study aims to review the recent techniques in digital multimedia compression with respect to fractal coding techniques. It covers the proposed fractal coding methods in audio and image domain and the techniques that were used for accelerating the encoding time process which is considered the main challenge in fractal compression. This study also presents the trends of the researcher's interests in applying fractal coding in image domain in comparison to the audio domain. This review opens directions for further researches in the area of fractal coding in audio compression and removes the obstacles that face its implementation in order to compare fractal audio with the renowned audio compression techniques.

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Optimizing Apple Lossless Audio Codec Algorithm using NVIDIA CUDA Architecture

Cited by 6 publications

References 11 publications

Real-Time Implementation and Performance Optimization of Local Derivative Pattern Algorithm on GPUs

Real-Time Implementation and Performance Optimization of Local Derivative Pattern Algorithm on GPUs

DAMix: Exploiting Deep Autoregressive Model Zoo for Improving Lossless Compression Generalization

Image and audio fractal compression: comprehensive review, enhancements and research directions

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