Efficient JPEG2000 EBCOT Context Modeling for Massively Parallel Architectures

Matela, Jiří; Rusňák, Vít; Holub, Petr

doi:10.1109/dcc.2011.49

Cited by 16 publications

(8 citation statements)

References 8 publications

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“…Matela et al have done a lot of work on accelerating parts of JPEG 2000 using GPU. Matela (2009) focuses on a two-dimensional DWT, while in Matela et al (2011a) they reformulate context modeling, a part of Tier-1, such that it becomes appropriate for massive parallel architectures. Finally, Matela et al (2011b) present an efficient implementation of arithmetical coding, another part of Tier-1, on GPU.…”

Section: Related Workmentioning

confidence: 99%

“…However, our codec does not use these modes because they decrease the compression performance and at the same time increase the code complexity. There exists work that reformulates the algorithm such that it exhibits parallelism for a single code-block (Matela et al, 2011a; Le et al, 2011; Wei et al, 2012). This is only possible for encoding because it requires complete information, for decoding an equivalent approach is impossible.…”

Section: Parallelizationmentioning

confidence: 99%

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Heterogeneous acceleration of volumetric JPEG 2000 using OpenCL

Cornelis

Lemeire

Bruylants

et al. 2016

The International Journal of High Performance Computing Applica

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This paper discusses an OpenCL version of a volumetric JPEG 2000 codec that runs on GPUs, multi-core processors or a combination of both. Since the performance critical part consists of a fine-grained (discrete wavelet transform) and coarse-grained algorithm (Tier-1), the best performance is obtained with a hybrid execution in which the discrete wavelet transform is executed on a GPU and Tier-1 on a multi-core. Using an Intel i7 multi-core in combination with a modest NVIDIA Quadro K620 GPU yields speedups greater than 10 compared with the original sequential code. The performance bottlenecks that arise on GPUs when parallelizing algorithms that are coarse-grained by nature are discussed and also the optimizations that are possible. A performance analysis reveals the inefficiencies and explains the deviations from the GPU peak performance.

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Section: Related Workmentioning

confidence: 99%

Section: Parallelizationmentioning

confidence: 99%

Heterogeneous acceleration of volumetric JPEG 2000 using OpenCL

Cornelis

Lemeire

Bruylants

et al. 2016

The International Journal of High Performance Computing Applica

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show abstract

“…The JPEG XR standard is implemented in [16], and video coding standards are studied in [17], [18]. Other coding schemes such as EBCOT and wavelet lower trees are also implemented in GPUs in [19] and [20], respectively. Such implementations reduce the execution time of CPU-based implementations.…”

Section: Introductionmentioning

confidence: 99%

Bitplane Image Coding With Parallel Coefficient Processing

Aulí-Llinàs

Enfedaque

Moure

et al. 2016

IEEE Trans. on Image Process.

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“…Even so, the current approach is to implement already developed coding systems for their execution in SIMD-based processors. Without aiming to be exhaustive, GPU implementations of JPEG2000 are found in [4], [5] and there exist commercial products like [6] as well. The JPEG XR standard is implemented in [7], and video coding standards are studied in [3], [8].…”

Section: Introductionmentioning

confidence: 99%