Adaptive Multi-Pattern Fast Block-Matching Algorithm Based on Motion Classification Techniques

Motion estimation is the most time-consuming subsystem in a video codec. Thus, more efficient methods of motion estimation should be investigated. Real video sequences usually exhibit a wide-range of motion content as well as different degrees of detail, which become particularly difficult to manage by typical block-matching algorithms. Recent developments in the area of motion estimation have focused on the adaptation to video contents. Adaptive thresholds and multi-pattern search algorithms have shown to achieve good performance when they success to adjust to motion characteristics. This paper proposes an adaptive algorithm, called MCS, that makes use of an especially tailored classifier that detects some motion cues and chooses the search pattern that best fits to them. Specifically, a hierarchical structure of binary linear classifiers is proposed. Our experimental results show that MCS notably reduces the computational cost with respect to an state-of-the-art method while maintaining the qualityPublicad

Video Classification With CNNs: Using The Codec As A Spatio-Temporal Activity Sensor

We investigate video classification via a two-stream convolutional neural network (CNN) design that directly ingests information extracted from compressed video bitstreams. Our approach begins with the observation that all modern video codecs divide the input frames into macroblocks (MBs). We demonstrate that selective access to MB motion vector (MV) information within compressed video bitstreams can also provide for selective, motion-adaptive, MB pixel decoding (a.k.a., MB texture decoding). This in turn allows for the derivation of spatio-temporal video activity regions at extremely high speed in comparison to conventional full-frame decoding followed by optical flow estimation. In order to evaluate the accuracy of a video classification framework based on such activity data, we independently train two CNN architectures on MB texture and MV correspondences and then fuse their scores to derive the final classification of each test video. Evaluation on two standard datasets shows that the proposed approach is competitive to the best two-stream video classification approaches found in the literature. At the same time: (i) a CPU-based realization of our MV extraction is over 977 times faster than GPU-based optical flow methods; (ii) selective decoding is up to 12 times faster than full-frame decoding; (iii) our proposed spatial and temporal CNNs perform inference at 5 to 49 times lower cloud computing cost than the fastest methods from the literature.Comment: Accepted in IEEE Transactions on Circuits and Systems for Video Technology. Extension of ICIP 2017 conference pape

Fusion-Based Versatile Video Coding Intra Prediction Algorithm with Template Matching and Linear Prediction

The new generation video coding standard Versatile Video Coding (VVC) has adopted many novel technologies to improve compression performance, and consequently, remarkable results have been achieved. In practical applications, less data, in terms of bitrate, would reduce the burden of the sensors and improve their performance. Hence, to further enhance the intra compression performance of VVC, we propose a fusion-based intra prediction algorithm in this paper. Specifically, to better predict areas with similar texture information, we propose a fusion-based adaptive template matching method, which directly takes the error between reference and objective templates into account. Furthermore, to better utilize the correlation between reference pixels and the pixels to be predicted, we propose a fusion-based linear prediction method, which can compensate for the deficiency of single linear prediction. We implemented our algorithm on top of the VVC Test Model (VTM) 9.1. When compared with the VVC, our proposed fusion-based algorithm saves a bitrate of 0.89%, 0.84%, and 0.90% on average for the Y, Cb, and Cr components, respectively. In addition, when compared with some other existing works, our algorithm showed superior performance in bitrate savings

Light field coding with field of view scalability and exemplar-based inter-layer prediction

Light field imaging based on microlens arrays—a.k.a. holoscopic, plenoptic, and integral imaging—has currently risen up as a feasible and prospective technology for future image and video applications. However, deploying actual light field applications will require identifying more powerful representations and coding solutions that support arising new manipulation and interaction functionalities. In this context, this paper proposes a novel scalable coding solution that supports a new type of scalability, referred to as field-of-view scalability. The proposed scalable coding solution comprises a base layer compliant with the High Efficiency Video Coding (HEVC) standard, complemented by one or more enhancement layers that progressively allow richer versions of the same light field content in terms of content manipulation and interaction possibilities. In addition, to achieve high-compression performance in the enhancement layers, novel exemplar-based interlayer coding tools are also proposed, namely: 1) a direct prediction based on exemplar texture samples from lower layers and 2) an interlayer compensated prediction using a reference picture that is built relying on an exemplar-based algorithm for texture synthesis. Experimental results demonstrate the advantages of the proposed scalable coding solution to cater to users with different preferences/requirements in terms of interaction functionalities, while providing better rate- distortion performance (independently of the optical setup used for acquisition) compared to HEVC and other scalable light field coding solutions in the literature.info:eu-repo/semantics/acceptedVersio

Video Traffic Characteristics of Modern Encoding Standards: H.264/AVC with SVC and MVC Extensions and H.265/HEVC

abstract: Video encoding for multimedia services over communication networks has significantly advanced in recent years with the development of the highly efficient and flexible H.264/AVC video coding standard and its SVC extension. The emerging H.265/HEVC video coding standard as well as 3D video coding further advance video coding for multimedia communications. This paper first gives an overview of these new video coding standards and then examines their implications for multimedia communications by studying the traffic characteristics of long videos encoded with the new coding standards. We review video coding advances from MPEG-2 and MPEG-4 Part 2 to H.264/AVC and its SVC and MVC extensions as well as H.265/HEVC. For single-layer (nonscalable) video, we compare H.265/HEVC and H.264/AVC in terms of video traffic and statistical multiplexing characteristics. Our study is the first to examine the H.265/HEVC traffic variability for long videos. We also illustrate the video traffic characteristics and statistical multiplexing of scalable video encoded with the SVC extension of H.264/AVC as well as 3D video encoded with the MVC extension of H.264/AVC.View the article as published at https://www.hindawi.com/journals/tswj/2014/189481

Efficient HEVC-based video adaptation using transcoding

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

Rate Control Initialization Algorithm for Scalable Video Coding

Proceeding of: 18th IEEE International Conference on Image Processing (ICIP), 2011.In this paper we propose a novel rate control initialization algorithm for real-time H.264/scalable video coding. In particular, a two-step approach is proposed. First, the initial quantization parameter (QP) for each layer is determined by means of a parametric rate-quantization (R-Q) modeling that depends on the layer identifier (base or enhancement) and on the type of scalability (spatial or quality). Second, an intra-frame QP refinement method that allows for adapting the initial QP value when needed is carried out over the three first coded frames in order to take into consideration both the buffer control and the spatio-temporal complexity of the scene. The experimental results show that the proposed R-Q modeling for initial QP estimation, in combination with the intra-frame QP refinement method, provide a good performance in terms of visual quality and buffer control, achieving remarkably similar results to those achieved by using ideal initial QP values.The Spanish National grant TSI-020110-2009-103 (AFICUS) and the Regional grant CCG10-UC3M/TIC-5570 (AMASSACA).Publicad