486 research outputs found

    Can you tell a face from a HEVC bitstream?

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    Image and video analytics are being increasingly used on a massive scale. Not only is the amount of data growing, but the complexity of the data processing pipelines is also increasing, thereby exacerbating the problem. It is becoming increasingly important to save computational resources wherever possible. We focus on one of the poster problems of visual analytics -- face detection -- and approach the issue of reducing the computation by asking: Is it possible to detect a face without full image reconstruction from the High Efficiency Video Coding (HEVC) bitstream? We demonstrate that this is indeed possible, with accuracy comparable to conventional face detection, by training a Convolutional Neural Network on the output of the HEVC entropy decoder

    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

    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

    Steered mixture-of-experts for light field images and video : representation and coding

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    Research in light field (LF) processing has heavily increased over the last decade. This is largely driven by the desire to achieve the same level of immersion and navigational freedom for camera-captured scenes as it is currently available for CGI content. Standardization organizations such as MPEG and JPEG continue to follow conventional coding paradigms in which viewpoints are discretely represented on 2-D regular grids. These grids are then further decorrelated through hybrid DPCM/transform techniques. However, these 2-D regular grids are less suited for high-dimensional data, such as LFs. We propose a novel coding framework for higher-dimensional image modalities, called Steered Mixture-of-Experts (SMoE). Coherent areas in the higher-dimensional space are represented by single higher-dimensional entities, called kernels. These kernels hold spatially localized information about light rays at any angle arriving at a certain region. The global model consists thus of a set of kernels which define a continuous approximation of the underlying plenoptic function. We introduce the theory of SMoE and illustrate its application for 2-D images, 4-D LF images, and 5-D LF video. We also propose an efficient coding strategy to convert the model parameters into a bitstream. Even without provisions for high-frequency information, the proposed method performs comparable to the state of the art for low-to-mid range bitrates with respect to subjective visual quality of 4-D LF images. In case of 5-D LF video, we observe superior decorrelation and coding performance with coding gains of a factor of 4x in bitrate for the same quality. At least equally important is the fact that our method inherently has desired functionality for LF rendering which is lacking in other state-of-the-art techniques: (1) full zero-delay random access, (2) light-weight pixel-parallel view reconstruction, and (3) intrinsic view interpolation and super-resolution

    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

    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

    Compression vidéo basée sur l'exploitation d'un décodeur intelligent

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    This Ph.D. thesis studies the novel concept of Smart Decoder (SDec) where the decoder is given the ability to simulate the encoder and is able to conduct the R-D competition similarly as in the encoder. The proposed technique aims to reduce the signaling of competing coding modes and parameters. The general SDec coding scheme and several practical applications are proposed, followed by a long-term approach exploiting machine learning concept in video coding. The SDec coding scheme exploits a complex decoder able to reproduce the choice of the encoder based on causal references, eliminating thus the need to signal coding modes and associated parameters. Several practical applications of the general outline of the SDec scheme are tested, using different coding modes during the competition on the reference blocs. Despite the choice for the SDec reference block being still simple and limited, interesting gains are observed. The long-term research presents an innovative method that further makes use of the processing capacity of the decoder. Machine learning techniques are exploited in video coding with the purpose of reducing the signaling overhead. Practical applications are given, using a classifier based on support vector machine to predict coding modes of a block. The block classification uses causal descriptors which consist of different types of histograms. Significant bit rate savings are obtained, which confirms the potential of the approach.Cette thèse de doctorat étudie le nouveau concept de décodeur intelligent (SDec) dans lequel le décodeur est doté de la possibilité de simuler l’encodeur et est capable de mener la compétition R-D de la même manière qu’au niveau de l’encodeur. Cette technique vise à réduire la signalisation des modes et des paramètres de codage en compétition. Le schéma général de codage SDec ainsi que plusieurs applications pratiques sont proposées, suivis d’une approche en amont qui exploite l’apprentissage automatique pour le codage vidéo. Le schéma de codage SDec exploite un décodeur complexe capable de reproduire le choix de l’encodeur calculé sur des blocs de référence causaux, éliminant ainsi la nécessité de signaler les modes de codage et les paramètres associés. Plusieurs applications pratiques du schéma SDec sont testées, en utilisant différents modes de codage lors de la compétition sur les blocs de référence. Malgré un choix encore simple et limité des blocs de référence, les gains intéressants sont observés. La recherche en amont présente une méthode innovante qui permet d’exploiter davantage la capacité de traitement d’un décodeur. Les techniques d’apprentissage automatique sont exploitées pour but de réduire la signalisation. Les applications pratiques sont données, utilisant un classificateur basé sur les machines à vecteurs de support pour prédire les modes de codage d’un bloc. La classification des blocs utilise des descripteurs causaux qui sont formés à partir de différents types d’histogrammes. Des gains significatifs en débit sont obtenus, confirmant ainsi le potentiel de l’approche
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