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IVC2-O: Image and Video Compression 2
First presentation: https://mmsp-virtual.org/presentation/oral/decoding-energy-optimal-video-encoding-x265
Second presentation: https://mmsp-virtual.org/presentation/oral/multispectral-image-compression-based-hevc-using-pelrecursive-interband
Third presentation: https://mmsp-virtual.org/presentation/oral/lowcomplexity-angular-intraprediction-convolutional-neural-network-lossless-hevc
Fourth presentation: https://mmsp-virtual.org/presentation/oral/towards-fast-and-efficient-vvc-encoding
9:40am - 9:55am
Decoding Energy Optimal Video Encoding for x265
Friedrich-Alexander University Erlangen-Nürnberg, Germany
This paper presents optimal x265-encoder configurations and an enhanced optimization algorithm for minimizing the software decoding energy of HEVC-coded videos. We reach this goal with two contributions. First, we perform a detailed analysis on the influence of various encoder settings on the decoding energy. Second, we include an enhanced version of an algorithm called decoding-energy-rate-distortion optimization into x265, which we optimize for fast and efficient encoding. This algorithm introduces the estimated decoding energy as an additional optimization criterion into the rate-distortion cost function. We evaluate the extended encoder in terms of bitrate, distortion, and decoding energy, where we perform energy measurements to prove the superior energy efficiency. We find that the combination of the ‘fastdecoding’ tuning option of x265 with the enhanced decoding-energy-rate-distortion optimization leads to 27.2% and 26.0% of decoding energy savings for OpenHEVC and HM decoding, respectively. At the same time, compression efficiency losses of 4.31% and negligible decreases in encoder runtime of 0.39% can be observed.
9:55am - 10:10am
⭐ This paper has been nominated for the best paper award.
Multispectral Image Compression Based on HEVC Using Pel-Recursive Inter-Band Prediction
FAU Erlangen-Nuremberg, Germany
Recent developments in optical sensors enable a wide range of applications for multispectral imaging, e.g., in surveillance, optical sorting, and life-science instrumentation. Increasing spatial and spectral resolution allows to create higher quality products, however, it poses challenges in handling such large amounts of data. Consequently, specialized compression techniques for multispectral images are required. High Efficiency Video Coding (HEVC) is known to be the state of the art in efficiency for both video coding and still image coding. In this paper, we propose a cross-spectral compression scheme for efficiently coding multispectral data based on HEVC. Extending intra picture prediction by a novel inter-band predictor, spectral as well as spatial redundancies can be effectively exploited. Dependencies among the current band and further spectral references are considered jointly by adaptive linear regression modeling. The proposed backward prediction scheme does not require additional side information for decoding. We show that our novel approach is able to outperform state-of-the-art lossy compression techniques in terms of rate-distortion performance. On different data sets, average Bjontegaard delta rate savings of 82 % and 55 % compared to HEVC and a reference method from literature are achieved, respectively.
10:10am - 10:25am
Low-Complexity Angular Intra-Prediction Convolutional Neural Network for Lossless HEVC
Vrije Universiteit Brussel, Belgium
The paper proposes a novel low-complexity Convolutional Neural Network (CNN) architecture for block-wise angular intra-prediction in lossless video coding. The proposed CNN architecture is designed based on an efficient patch processing layer structure. The proposed CNN-based prediction method is employed to process an input patch containing the causal neighborhood of the current block in order to directly generate the predicted block. The trained models are integrated in the HEVC video coding standard to perform CNN-based angular intra-prediction and to compete with the conventional HEVC prediction. The proposed CNN architecture contains a reduced number of parameters equivalent to only 37% of that of the state-of-the-art reference CNN architecture. Experimental results show that the inference runtime is also reduced by around 5.5% compared to that of the reference method. At the same time, the proposed coding systems yield 83% to 91% of the compression performance of the reference method. The results demonstrate the potential of structural and complexity optimizations in CNN-based intra-prediction for lossless HEVC.
10:25am - 10:40am
Towards Fast and Efficient VVC Encoding
1Fraunhofer Institute for Telecommunications HHI, Germany; 2Free University of Berlin
Versatile Video Coding (VVC) is a new international video coding standard to be finalized in July 2020. It is designed to provide around 50% bit-rate saving at the same subjective visual quality over its predecessor, High Efficiency Video Coding (H.265/HEVC). During the standard development, objective bit-rate savings of around 40% have been reported for the VVC reference software (VTM) compared to the HEVC reference software (HM). The unoptimized VTM encoder is around 9x, and the decoder around 2x, slower than HM. This paper discusses the VVC encoder complexity in terms of software runtime. The modular design of the standard allows a VVC encoder to trade off bit-rate savings and encoder runtime. Based on a detailed tradeoff analysis, results for different operating points are reported. Additionally, initial work on software and algorithm optimization is presented. With the optimized software algorithms, an operating point with an over 22x faster single-threaded encoder runtime than VTM can be achieved, i.e. around 2.5x faster than HM, while still providing more than 30% bit-rate savings over HM. Finally, our experiments demonstrate the flexibility of VVC and its potential for optimized software encoder implementations.
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