Motion estimation and CABAC VLSI co-processors for real-time high-quality H.264/AVC video coding

Real-time and high-quality video coding is gaining a wide interest in the research and industrial community for different applications. H.264/AVC, a recent standard for high performance video coding, can be successfully exploited in several scenarios including digital video broadcasting, high-definition TV and DVD-based systems, which require to sustain up to tens of Mbits/s. To that purpose this paper proposes optimized architectures for H.264/AVC most critical tasks, Motion estimation and context adaptive binary arithmetic coding. Post synthesis results on sub-micron CMOS standard-cells technologies show that the proposed architectures can actually process in real-time 720 × 480 video sequences at 30 frames/s and grant more than 50 Mbits/s. The achieved circuit complexity and power consumption budgets are suitable for their integration in complex VLSI multimedia systems based either on AHB bus centric on-chip communication system or on novel Network-on-Chip (NoC) infrastructures for MPSoC (Multi-Processor System on Chip

Studying error resilience performance for a feedback channel based transform domain Wyner-Ziv video codec

Wyner-Ziv (WZ) video coding is an emerging video coding paradigm based on two major Information Theory results: the Slepian-Wolf and Wyner-Ziv theorems. One of the most interesting and used WZ video i coding architectures makes use of a feedback channel (FC) to perform c rate control at the decoder; in this context, the Slepian-Wolf coding t module is typically based on turbo coding with puncturing. Because WZ coding is not based on the prediction loop used in conventional video coding but rather on a statistical approach where a decoder estimation of the frame to be coded is 'corrected' by the encoder, it provides intrinsic error resilience capabilities. This paper intends to study the error resilience performance of a feedback channel based transform domain WZ codec using appropriate scenarios and conditions, notably in comparison with the best performing H. 264/AVC standard.info:eu-repo/semantics/acceptedVersio

Increased compression efficiency of AVC and HEVC CABAC by precise statistics estimation

The paper presents Improved Adaptive Arithmetic Coding algorithm for application in future video compression technology. The proposed solution is based on the Context-based Adaptive Binary Arithmetic Coding (CABAC) technique and uses the authors’ mechanism of symbols probability estimation that exploits Context-Tree Weighting (CTW) technique. This paper proposes the version of the algorithm, that allows an arbitrary selection of depth of context trees, when activating the algorithm in the framework of the AVC or HEVC video encoders. The algorithm has been tested in terms of coding efficiency of data and its computational complexity. Results showed, that depending of depth of context trees from 0.1% to 0.86% reduction of bitrate is achieved, when using the algorithm in the HEVC video encoder and 0.4% to 2.3% compression gain in the case of the AVC. The new solution increases complexity of entropy encoder itself, however, this does not translate into increase the complexity of the whole video encoder

A Fully Scalable Video Coder with Inter-Scale Wavelet Prediction and Morphological Coding

In this paper a new fully scalable - wavelet based - video coding architecture is proposed, where motion compensated temporal filtered subbands of spatially scaled versions of a video sequence can be used as base layer for inter-scale predictions. These predictions take place between data at the same resolution level without the need of interpolation. The prediction residuals are further transformed by spatial wavelet decompositions. The resulting multi-scale spatiotemporal wavelet subbands are coded thanks to an embedded morphological dilation technique and context based arithmetic coding. Dyadic spatio-temporal scalability and progressive SNR scalability are achieved. Multiple adaptation decoding can be easily implemented without the need of knowing a predefined set of operating points. The proposed coding system allows to compensate some of the typical drawbacks of current wavelet based scalable video coding architectures and shows interesting visual results even when compared with the single operating point video coding standard AVC/H.264

Region-adaptive probability model selection for the arithmetic coding of video texture

In video coding systems using adaptive arithmetic coding to compress texture information, the employed symbol probability models need to be retrained every time the coding process moves into an area with different texture. To avoid this inefficiency, we propose to replace the probability models used in the original coder with multiple switchable sets of probability models. We determine the model set to use in each spatial region in an optimal manner, taking into account the additional signaling overhead. Experimental results show that this approach, when applied to H. 264/AVC's context-based adaptive binary arithmetic coder (CABAC), yields significant bit-rate savings, which are comparable to or higher than those obtained using alternative improvements to CABAC previously proposed in the literature

Multi-perspective cost-sensitive context-aware multi-instance sparse coding and its application to sensitive video recognition

With the development of video-sharing websites, P2P, micro-blog, mobile WAP websites, and so on, sensitive videos can be more easily accessed. Effective sensitive video recognition is necessary for web content security. Among web sensitive videos, this paper focuses on violent and horror videos. Based on color emotion and color harmony theories, we extract visual emotional features from videos. A video is viewed as a bag and each shot in the video is represented by a key frame which is treated as an instance in the bag. Then, we combine multi-instance learning (MIL) with sparse coding to recognize violent and horror videos. The resulting MIL-based model can be updated online to adapt to changing web environments. We propose a cost-sensitive context-aware multi- instance sparse coding (MI-SC) method, in which the contextual structure of the key frames is modeled using a graph, and fusion between audio and visual features is carried out by extending the classic sparse coding into cost-sensitive sparse coding. We then propose a multi-perspective multi- instance joint sparse coding (MI-J-SC) method that handles each bag of instances from an independent perspective, a contextual perspective, and a holistic perspective. The experiments demonstrate that the features with an emotional meaning are effective for violent and horror video recognition, and our cost-sensitive context-aware MI-SC and multi-perspective MI-J-SC methods outperform the traditional MIL methods and the traditional SVM and KNN-based methods

Model-independent rate control for intra-coding based on piecewise linear approximations

This paper proposes a rate control (RC) algorithm for intra-coded sequences (I-frames) within the context of block-based predictive transform coding that departs from using trained models to approximate the rate-distortion (R-D) characteristics of the video sequence. Our algorithm employs piecewise linear approximations of the rate-distortion (R-D) curve of a frame at the block-level. Specifically, it employs information about the rate and distortion of already compressed blocks within the current frame to linearly approximate the slope of the R-D curve of each block. The proposed algorithm is implemented in the High-Efficiency Video Coding (H.265/HEVC) standard and compared with its current RC algorithm, which is based on a trained model. Evaluations on a variety of intra-coded sequences show that the proposed RC algorithm not only attains the overall target bit rate more accurately than the RC algorithm used by H.265/HEVC algorithm but is also capable of encoding each I-frame at a more constant bit rate according to the overall bit budget

Saliency-Enabled Coding Unit Partitioning and Quantization Control for Versatile Video Coding

The latest video coding standard, versatile video coding (VVC), has greatly improved coding efficiency over its predecessor standard high efficiency video coding (HEVC), but at the expense of sharply increased complexity. In the context of perceptual video coding (PVC), the visual saliency model that utilizes the characteristics of the human visual system to improve coding efficiency has become a reliable method due to advances in computer performance and visual algorithms. In this paper, a novel VVC optimization scheme compliant PVC framework is proposed, which consists of fast coding unit (CU) partition algorithm and quantization control algorithm. Firstly, based on the visual saliency model, we proposed a fast CU division scheme, including the redetermination of the CU division depth by calculating Scharr operator and variance, as well as the executive decision for intra sub-partitions (ISP), to reduce the coding complexity. Secondly, a quantization control algorithm is proposed by adjusting the quantization parameter based on multi-level classification of saliency values at the CU level to reduce the bitrate. In comparison with the reference model, experimental results indicate that the proposed method can reduce about 47.19% computational complexity and achieve a bitrate saving of 3.68% on average. Meanwhile, the proposed algorithm has reasonable peak signal-to-noise ratio losses and nearly the same subjective perceptual quality