Reduced Complexity Superresolution for Low-Bitrate Video Compression

Georgis, Georgios; Lentaris, George; Reisis, D.

doi:10.1109/tcsvt.2015.2389431

Cited by 34 publications

(29 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…3 The L-SEABI Algorithm as a standalone SR solution and State-of-the-Art enhancement Our proposed L-SEABI Super-Resolution algorithm [6], achieves objective quality comparable to highly involved SR methods at significantly lower computational cost, i.e. 3 orders of magnitude less execution time.…”

Section: Gpu-fpga Comparisonmentioning

confidence: 97%

“…Focusing on improving solutions for image processing problems, this work presents parallelization techniques for the low-complexity SIL-SEABI SR algorithm [6]. As a preface to the SIL-SEABI parallelization, the following section stresses the advantages of using it as an enhancement to well-established and more recent state-of-the-art SR techniques.…”

Section: Gpu-fpga Comparisonmentioning

confidence: 99%

“…It consists of a construction phase which is executed once and a refinement phase which is iterative. The single iteration case of L-SEABI [6], namely SIL-SEABI, further reduces the execution time by an order of magnitude at the expense of negligible quality degradation.…”

Section: Gpu-fpga Comparisonmentioning

confidence: 99%

“…Comparison to the state-of-the-art shows [6] that L-SEABI is faster than a) dictionary-based methods such as the Sparse Representation with over-complete dictionaries [32], the Nonlocal Similarity Adaptive Regularization [33] (ASDS-AR-NL) and the Anchored Neighborhood Regression (ANR) [2], b) Bayesian prior methods such as the Norm l1 [34], and better than c) IBPbased methods such as the Nonlocal Iterative BackProjection [35] (NLIBP ). More recently, the authors in [36] proposed the concept of a nonlocal autoregressive model (NARM ) i.e.…”

Section: Gpu-fpga Comparisonmentioning

confidence: 99%

“…It emphasizes the benefits of employing our Low-complexity Statistical Edge-Adaptive Back-projected Interpolation (L-SEABI ) SR method [6] to speed up the reconstruction process by presenting efficient parallelization techniques for this method performing on GPUs and FPGAs. Our goal is to provide techniques and the corresponding implementations, which overcome the underlying platform's drawbacks and ultimately, to present a comprehensive performance assessment.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Acceleration techniques and evaluation on multi-core CPU, GPU and FPGA for image processing and super-resolution

Georgis

Lentaris

Reisis

2016

J Real-Time Image Proc

Self Cite

View full text Add to dashboard Cite

Super-Resolution (SR) techniques constitute a key element in image applications, which need highresolution reconstruction while in the worst case only a single low-resolution observation is available. SR techniques involve computationally demanding processes and thus researchers are currently focusing on SR performance acceleration. Aiming at improving the SR performance, the current paper builds up on the characteristics of the L-SEABI Super-Resolution (SR) method to introduce parallelization techniques for GPUs and FPGAs. The proposed techniques accelerate GPU reconstruction of Ultra-High Definition content, by achieving three (3x) times faster than the real-time performance on mid-range and previous generation devices and at least nine times (9x) faster than the real-time performance on high-end GPUs. The FPGA design leads to a scalable architecture performing four (4x) times faster than the real-time on low-end Xilinx Virtex 5 devices and sixty-nine times (69x) faster than the real-time on the Virtex 2000t. Moreover, we confirm the benefits of the proposed acceleration techniques by employing them on a different category of image-processing algorithms: on window-based Disparity functions, for which the proposed GPU technique shows an improvement over the CPU performance ranging from 14 times (14x) to 64 times (64x) while the proposed FPGA architecture provides 29x acceleration.

show abstract

Section: Gpu-fpga Comparisonmentioning

confidence: 97%

Section: Gpu-fpga Comparisonmentioning

confidence: 99%

Section: Gpu-fpga Comparisonmentioning

confidence: 99%