Georgios Georgis

Reduced Complexity Superresolution for Low-Bitrate Video Compression

Evolving video applications impose requirements for high image quality, low bitrate, and/or small computational cost. This paper combines state-of-the-art coding and superresolution (SR) techniques to improve video compression both in terms of coding efficiency and complexity. The proposed approach improves a generic decimation-quantization compression scheme by introducing low complexity single-image SR techniques for rescaling the data at the decoder side and by jointly exploring/optimizing the downsampling/upsampling processes. The enhanced scheme achieves improvement of the quality and systems complexity compared with conventional codecs and can be easily modified to meet various diverse requirements, such as effectively supporting any off-the-shelf video codec, for instance H.264/Advanced Video Coding or High Efficiency Video Coding. Our approach builds on studying the generic schemes parameterization with common rescaling techniques to achieve 2.4-dB peak signal-to-noise ratio (PSNR) quality improvement at low-bitrates compared with the conventional codecs and proposes a novel SR algorithm to advance the critical bitrate at the level of 10 Mb/s. The evaluation of the SR algorithm includes the comparison of its performance to other image rescaling solutions of the literature. The results show quality improvement by 5-dB PSNR over straightforward interpolation techniques and computational time reduction by three orders of magnitude when compared with the highly involved methods of the field. Therefore, our algorithm proves to be most suitable for use in reduced complexity downsampled compression schemes.

Study of interpolation filters for motion estimation with application in H.264/AVC encoders

The current paper studies low-complexity image super-resolution techniques for improving the motion estimation process of video encoding. Aiming at speeding up the generation of candidate blocks during the computationally intensive search algorithm, we present interpolation techniques with reduced cost compared to standard 6-tap filtering procedures. Furthermore, we compare their performance to that of commonly used half-pixel interpolation techniques with respect to the resulting image quality and the processing time. The research has been based on using a typical fractional motion estimation algorithm preceding the processing of the H.264/AVC standard motion compensation, and thus, the research benefits the design of H.264/AVC encoders.

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

Super-resolution (SR) techniques constitute a key element in image applications, which need high-resolution 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 SR method to introduce parallelization techniques for GPUs and FPGAs. The proposed techniques accelerate GPU reconstruction of ultra-high definition content, by achieving three (3

Single-image super-resolution using low complexity adaptive iterative back-projection

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Towards real-time neuronal connectivity assessment: A scalable pipelined parallel generalized partial directed coherence engine

×) times faster than the real-time performance on mid-range and previous generation devices and at least nine times (9

Low Complexity Interpolation Filters for Motion Estimation and Application to the H.264 Encoders

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