Discrete wavelet transform-based fast and high-efficient lossless intraframe compression algorithm for high-efficiency video coding

Xu, Weizhe; Fu, Fang; Wang, Yao; Wang, Jinxiang

doi:10.1117/1.jei.28.1.013017

Cited by 2 publications

(1 citation statement)

References 24 publications

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“…DWT (Discrete Wavelet Transform) was first utilized for fast nearest-neighbor search in medical image databases by Korn et al [41], then Chan and Fu [5] applied it for the compression of time series. Besides, Jain et al [12] and Rajan and Fred [13] used 2D-DWT to compress image, and Xu et al [14] presented a DWT-based fast and high-efficient intraframe compression algorithm. In contrast to DFT, DWT is a local transformation.…”

Section: Related Workmentioning

confidence: 99%

Fast Piecewise Polynomial Fitting of Time-Series Data for Streaming Computing

Gao

Zhang

et al. 2020

IEEE Access

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Streaming computing attracts intense attention because of the demand for massive data analyzing in real-time. Due to unbounded and continuous input, the volume of streaming data is so high that all the data cannot be permanently stored. Piecewise polynomial fitting is a popular data compression method that approximately represents the raw data stream with multiple polynomials. The polynomial coefficients corresponding to the best-fitting curve can be calculated by the method of least squares, which minimizes the sum of the squared residuals between observed and fitted values. However, built on several matrix calculations, the method of least squares always leads to high time complexity and is difficult to be applied to streaming computing. This paper puts forward a fast piecewise polynomial fitting for time-series data in streaming computing. The input data stream is dynamically segmented according to a given residual bound. Meanwhile, the data points in each segment are fitted using an improved polynomial fitting method, which has less time overhead than general polynomial fitting by reusing the intermediate calculation results. Experimental results on four time-series datasets show that our algorithm can achieve the highest speedup to the general piecewise polynomial fitting of 2.82x for periodically sampled time-series data and 1.85x for aperiodically sampled time-series data, without affecting the compression ratio and fitting accuracy. Moreover, the event-time latency comparison in a streaming environment indicates that the improved method can endure higher throughput than general piecewise polynomial fitting with the same latency. INDEX TERMS Least squares, piecewise polynomial fitting, streaming computing, time-series data.

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Section: Related Workmentioning

confidence: 99%

Fast Piecewise Polynomial Fitting of Time-Series Data for Streaming Computing

Gao

Zhang

et al. 2020

IEEE Access

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Efficient Low Bit-Rate Intra-Frame Coding using Common Information for 360-degree Video

Afsana

Paul

Murshed

et al. 2020

2020 IEEE 22nd International Workshop on Multimedia Signal Processing (MMSP)

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With the growth of video technologies, superresolution videos, including 360-degree immersive video has become a reality due to exciting applications such as augmented/virtual/mixed reality for better interaction and a wideangle user-view experience of a scene compared to traditional video with narrow-focused viewing angle. The new generation video contents are bandwidth-intensive in nature due to high resolution and demand high bit rate as well as low latency delivery requirements that pose challenges in solving the bottleneck of transmission and storage burdens. There is limited optimisation space in traditional video coding schemes for improving video coding efficiency in intra-frame due to the fixed size of processing block. This paper presents a new approach for improving intra-frame coding especially at low bit rate video transmission for 360-degree video for lossy mode of HEVC. Prior to using traditional HEVC intra-prediction, this approach exploits the global redundancy of entire frame by extracting common important information using multi-level discrete wavelet transformation. This paper demonstrates that the proposed method considering only low frequency information of a frame and encoding this can outperform the HEVC standard at low bit rates. The experimental results indicate that the proposed intra-frame coding strategy achieves an average of 54.07% BDrate reduction and 2.84 dB BD-PSNR gain for low bit rate scenario compared to the HEVC. It also achieves a significant improvement in encoding time reduction of about 66.84% on an average. Moreover, this finding also demonstrates that the existing HEVC block partitioning can be applied in the transform domain for better exploitation of information concentration as we applied HEVC on wavelet frequency domain.

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Discrete wavelet transform-based fast and high-efficient lossless intraframe compression algorithm for high-efficiency video coding

Cited by 2 publications

References 24 publications

Fast Piecewise Polynomial Fitting of Time-Series Data for Streaming Computing

Fast Piecewise Polynomial Fitting of Time-Series Data for Streaming Computing

Efficient Low Bit-Rate Intra-Frame Coding using Common Information for 360-degree Video

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