458 research outputs found

    Complexity Analysis Of Next-Generation VVC Encoding and Decoding

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    While the next generation video compression standard, Versatile Video Coding (VVC), provides a superior compression efficiency, its computational complexity dramatically increases. This paper thoroughly analyzes this complexity for both encoder and decoder of VVC Test Model 6, by quantifying the complexity break-down for each coding tool and measuring the complexity and memory requirements for VVC encoding/decoding. These extensive analyses are performed for six video sequences of 720p, 1080p, and 2160p, under Low-Delay (LD), Random-Access (RA), and All-Intra (AI) conditions (a total of 320 encoding/decoding). Results indicate that the VVC encoder and decoder are 5x and 1.5x more complex compared to HEVC in LD, and 31x and 1.8x in AI, respectively. Detailed analysis of coding tools reveals that in LD on average, motion estimation tools with 53%, transformation and quantization with 22%, and entropy coding with 7% dominate the encoding complexity. In decoding, loop filters with 30%, motion compensation with 20%, and entropy decoding with 16%, are the most complex modules. Moreover, the required memory bandwidth for VVC encoding/decoding are measured through memory profiling, which are 30x and 3x of HEVC. The reported results and insights are a guide for future research and implementations of energy-efficient VVC encoder/decoder.Comment: IEEE ICIP 202

    Quality of Experience (QoE)-Aware Fast Coding Unit Size Selection for HEVC Intra-prediction

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    The exorbitant increase in the computational complexity of modern video coding standards, such as High Efficiency Video Coding (HEVC), is a compelling challenge for resource-constrained consumer electronic devices. For instance, the brute force evaluation of all possible combinations of available coding modes and quadtree-based coding structure in HEVC to determine the optimum set of coding parameters for a given content demand a substantial amount of computational and energy resources. Thus, the resource requirements for real time operation of HEVC has become a contributing factor towards the Quality of Experience (QoE) of the end users of emerging multimedia and future internet applications. In this context, this paper proposes a content-adaptive Coding Unit (CU) size selection algorithm for HEVC intra-prediction. The proposed algorithm builds content-specific weighted Support Vector Machine (SVM) models in real time during the encoding process, to provide an early estimate of CU size for a given content, avoiding the brute force evaluation of all possible coding mode combinations in HEVC. The experimental results demonstrate an average encoding time reduction of 52.38%, with an average Bjøntegaard Delta Bit Rate (BDBR) increase of 1.19% compared to the HM16.1 reference encoder. Furthermore, the perceptual visual quality assessments conducted through Video Quality Metric (VQM) show minimal visual quality impact on the reconstructed videos of the proposed algorithm compared to state-of-the-art approaches

    Reducing the complexity of a multiview H.264/AVC and HEVC hybrid architecture

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    With the advent of 3D displays, an efficient encoder is required to compress the video information needed by them. Moreover, for gradual market acceptance of this new technology, it is advisable to offer backward compatibility with existing devices. Thus, a multiview H.264/Advance Video Coding (AVC) and High Efficiency Video Coding (HEVC) hybrid architecture was proposed in the standardization process of HEVC. However, it requires long encoding times due to the use of HEVC. With the aim of tackling this problem, this paper presents an algorithm that reduces the complexity of this hybrid architecture by reducing the encoding complexity of the HEVC views. By using Na < ve-Bayes classifiers, the proposed technique exploits the information gathered in the encoding of the H.264/AVC view to make decisions on the splitting of coding units in HEVC side views. Given the novelty of the proposal, the only similar work found in the literature is an unoptimized version of the algorithm presented here. Experimental results show that the proposed algorithm can achieve a good tradeoff between coding efficiency and complexity

    Efficient HEVC-based video adaptation using transcoding

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    In a video transmission system, it is important to take into account the great diversity of the network/end-user constraints. On the one hand, video content is typically streamed over a network that is characterized by different bandwidth capacities. In many cases, the bandwidth is insufficient to transfer the video at its original quality. On the other hand, a single video is often played by multiple devices like PCs, laptops, and cell phones. Obviously, a single video would not satisfy their different constraints. These diversities of the network and devices capacity lead to the need for video adaptation techniques, e.g., a reduction of the bit rate or spatial resolution. Video transcoding, which modifies a property of the video without the change of the coding format, has been well-known as an efficient adaptation solution. However, this approach comes along with a high computational complexity, resulting in huge energy consumption in the network and possibly network latency. This presentation provides several optimization strategies for the transcoding process of HEVC (the latest High Efficiency Video Coding standard) video streams. First, the computational complexity of a bit rate transcoder (transrater) is reduced. We proposed several techniques to speed-up the encoder of a transrater, notably a machine-learning-based approach and a novel coding-mode evaluation strategy have been proposed. Moreover, the motion estimation process of the encoder has been optimized with the use of decision theory and the proposed fast search patterns. Second, the issues and challenges of a spatial transcoder have been solved by using machine-learning algorithms. Thanks to their great performance, the proposed techniques are expected to significantly help HEVC gain popularity in a wide range of modern multimedia applications
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