Search

SHVC, the scalable extension of High Efficiency Video Coding (HEVC), uses advanced inter-layer prediction features in addition to the advanced compression tools of HEVC to improve the compression performance. Using combined features has brought us improved compression performance at the cost of huge computational complexity for the SHVC encoder. This complexity is mainly because of the the inter/intra-prediction mode search of the coding units. The focus of this study is on developing an efficient complexity reduction for quality scalability of SHVC encoder, with the intention to facilitate the adoption of SHVC for real-time applications. In this regard, first, we build a probabilistic model that uses the mode information and motion homogeneity of already encoded blocks in the enhancement layer (EL) and the base layer to predict the probabilities of all the available inter/intra modes of the to-be-coded block in the EL. Then, we propose an online-learning-based fast mode, assigning (FMA) method that uses the proposed probabilistic model to predict the mode of the to-be-coded block in the EL. Performance evaluation shows that our proposed FMA method reduces the total execution time of the SHVC encoder by 45.40% on average compared with unmodified SHVC codec while maintaining the overall video quality. [ABSTRACT FROM PUBLISHER]

Providing scalable video streaming services for heterogeneous users in dynamic networked environments requires efficient and adaptive quality management mechanisms which deliver quality-customized services according to the client’s preferences and adapt the services to cope with various network conditions. In this paper, we address the issue of quality adaptation for providing personalized scalable media streaming services in dynamic network environments. We propose a differentiated adaptive quality optimization algorithm, called Scalable Video Coding Quality Adaptation algorithm (SVC-QA), which adapts streaming quality based on both system-level and client-level optimization to optimize streaming quality according to network bandwidth conditions, content characteristics, a user’s quality preferences, and buffering capacities of different client devices (e.g., mobile phones, PCs, HDTVs, etc.). Comparative studies are conducted to compare our proposed algorithms with other adaptive methods. We show that two-level SVC quality adaptation method can achieve better SVC streaming quality with both high peak signal-to-noise ratio (PSNR) and low quality variance under dynamic resource constraints. Moreover, the proposed distributed method reduces the computational complexities at the server side substantially, making it practical and flexible for providing scalable streaming services. [ABSTRACT FROM AUTHOR]

This paper presents a complexity-aware adaptive spatial preprocessing scheme for the efficient scalable video coding (SVC) by employing an adaptive prefilter for each SVC layer. According to the presented scheme, a dynamic transition region is defined between the region of interest (ROI) and background within each video frame, and then various parameters of each prefilter (such as the standard deviation, kernel matrix size, and also a number of filters for the dynamic preprocessing of a transition region between the ROI and the background) are adaptively varied. The presented scheme has proved to be very efficient because it is based on an SVC computational complexity-rate-distortion analysis, thereby adding a complexity dimension to the conventional SVC rate-distortion analysis. As a result, the encoding computational complexity resources are significantly reduced, which is especially useful for portable encoders with limited power resources. The performance of the presented adaptive spatial preprocessing scheme is evaluated and tested in detail from both computational complexity and visual presentation quality points of view, further comparing it with the joint scalable video model reference software (JSVM 9.19) and demonstrating significant improvements. [ABSTRACT FROM PUBLISHER]

The video coding standard, H.264/AVC scalable video extension (SVC), adopts various advanced interlayer prediction modes to explore the data redundancies between layers for better coding efficiency but at the expense of significantly increased computational complexity and data access bandwidth, especially for hardware realization of fractional motion estimation mode decision. To deal with this problem, this paper proposes a mode preselection algorithm for fractional motion estimation in scalable video coding. We first analyze the rate distortion cost relationship between different prediction modes. With the statistical results, several mode preselection rules are proposed to filter out the potentially skippable prediction modes. Simulation results show that our proposed algorithm reduces up to 65.97% prediction modes and 79.79% coding time on average with only 0.036 dB and 0.496% BD-peak signal-to-noise ratio (PSNR) degradation and BD-rate increase, respectively. Furthermore, the proposed mode preselection algorithm has been implemented in hardware and it costs only 9 k gate counts, when synthesized by 90 nm CMOS technology. [ABSTRACT FROM PUBLISHER]

Transcoding of H.264/AVC bitstreams is frequently used for adapting video streams to varying network conditions or different end-user device characteristics. Transcoding requires a relatively high complexity (and thus high operational cost) for each adaptation operation, and possibly reduces the video quality. In comparison, transcoding the input bitstream to SVC requires only one adaptation step. Afterwards, the resulting SVC bitstream can be adapted instantly. To reduce the H.264/AVC-to-SVC transcoding complexity, a transcoding architecture which combines existing open-loop transcoding techniques with optimized closed-loop transcoding is proposed. Furthermore, this approach allows to scale the complexity of the system. Doing so, compared with a cascaded decoder-encoder only 8.48% of the complexity is required for the highest complexity, while a high rate-distortion efficiency is maintained and a high degree of scalability is guaranteed. When only limited resources are available, the transcoding complexity can be scaled such that over 99% of the complexity is reduced compared to a cascaded decoder-encoder. [ABSTRACT FROM PUBLISHER]

A fast mode decision algorithm for efficient implementation of the scalable video coding (SVC) extension of H.264/AVC is described. SVC incorporates interlayer prediction, a new tool that exploits as much lower layer information as possible in order to improve the coding efficiency of the enhancement layer. However, it also greatly increases the computational complexity. A fast mode selection algorithm that exploits the correlation of a macroblock in the enhancement layer and both the colocated macroblocks in the base layer and neighboring macroblocks in the enhancement layer is proposed. The algorithm examines the level of picture details and motion activity, and utilizes the mode information of the base layer to make faster enhancement layer decisions and thus save coding time. Simulation results show that the proposed algorithm reduces encoding by up to 84% compared with the JSVM 9.18 implementation. This is achieved without any noticeable degradation in rate distortion. [ABSTRACT FROM PUBLISHER]

Although the temporal scalability coding supported by both H.264/AVC and H.264/SVC provides a good means of adapting bitstream scalability in temporal dimension, it entails heavy computational complexity due to bidirectional motion estimation and compensation for B-pictures in temporal scalability levels. To make it more applicable, this paper introduces a fast inter-prediction mode decision method which is efficiently performed using statistical hypothesis testing. The hypothesis testing is performed on mean and variance of pixels in 16 \times 16 and 8 \times 8 blocks to decide early termination against further processing for their sub-blocks. Fallback check is employed to compromise the tradeoff between the compression efficiency and encoding time saving. The proposed method exhibits effective performance for early termination in the inter-prediction mode decision, thus leading to a significant reduction of the total encoding time up to average 64.8% with small increments in bit amounts and small degradation in visual quality. [ABSTRACT FROM PUBLISHER]

A joint model of scalable video coding (SVC) uses exhaustive mode and motion searches to select the best prediction mode and motion vector for each macroblock (MB) with high coding efficiency at the cost of computational complexity. If major characteristics of a coding MB such as the complexity of the prediction mode and the motion property can be identified and used in adjusting motion estimation (ME), one can design an algorithm that can adapt coding parameters to the video content. This way, unnecessary mode and motion searches can be avoided. In this paper, we propose a content-adaptive ME for SVC, including analyses of mode complexity and motion property to assist mode and motion searches. An experimental analysis is performed to study interlayer and spatial correlations in the coding information. Based on the correlations, the motion and mode characteristics of the current MB are identified and utilized to adjust each step of ME at the enhancement layer including mode decision, search-range selection, and prediction direction selection. Experimental results show that the proposed algorithm can significantly reduce the computational complexity of SVC while maintaining nearly the same rate distortion performance as the original encoder. [ABSTRACT FROM PUBLISHER]

Multiple description coding (MDC) decomposes one single media into several descriptions and transmits them over different channels for error resilience. Each description contributes to improving the reconstructed media quality when decoded. Distributed video coding (DVC) encodes multiple correlated images and utilizes error correction codes to shift the codec complexity to a joint decoder. Combining MDC with DVC (MDVC) yields a stable codec for mobile encoders. In this paper, to improve the MDVC codec performance, image correlations among the MDVC processing modules were exploited to improve reconstructed video quality and enhance transmission robustness. At the side encoder, a DVC-based adaptive differential pulse code modulation was designed to remove interframe redundancy to enhance rate-distortion performances. For the MDVC central decoding, intradescription and interdescription correlations were utilized to dynamically select the best reconstructed frames from two descriptions, instead of selecting just one description or all key-frames from two descriptions. Experiments showed that, as compared to previous methods, the proposed MDVC control method yielded 1–2 dB higher in image PSNRs for Wyner–Ziv reconstructed frames at the side decoder when encoding low-to-medium complexity videos. For high-complexity videos, it effectively prevents error correction of Wyner–Ziv frames from malfunctioning and yields about 3 dB higher in PSNR. The proposed MDVC central decoder control yields 1–4 dB higher PSNRs, as compared to side decoders. Under lossy transmission, it demonstrates 27–64% smaller PSNR variations, as compared to that of combining key-frames as the decoded video. The proposed MDVC system and control not only improve the DVC reconstructed video quality, but also reduce the quality fluctuation artifacts of MDC coded video for mobile coders. [ABSTRACT FROM PUBLISHER]

Scalable Video Coding is an H.264/AVC scalable extension that is used to provide various network-friendly scalability using a single bit stream. In SVC, the newly adapted predictive coding techniques can achieve high encoding efficiency, but they increase the computational complexity. To reduce the computational complexity, we present a fast mode decision algorithm based on the block complexity function considering the degree of correlation between base layer and enhancement layer. The simulation results show that the encoding time of the proposed fast mode decision algorithm for the combined scalability is about 54.72% compared with normal method although the loss of visual quality is negligible. [ABSTRACT FROM PUBLISHER]