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This study presents a fast video transcoding architecture that overcomes the complexity of different coding structures between H.264/AVC and SVC. The proposed algorithms simplify the mode decision process in SVC owing to its heavy computations. Two scenarios namely transcoding with the same quantization parameter and bitrate reduction are considere...
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در این مقاله، نامتعادلی بار شبکه¬ی توزیع با روش نوینی مبتنی بر جبران¬سازی توان راکتیو شبکه با استفاده از خازن-های تک¬فاز کلیدزنی کاهش پیدا می¬کند. برای این منظور، خازن¬های تک¬فاز با اتصال مثلث برای حذف مولفه¬هاي منفي جريان ها در فازها نصب می¬شود. تکنیک پیشنهادی برای کاهش نامتعادلی به صورت لحظه¬ای که نتایج آن نصب جبران¬کننده¬ی وار استاتیک (SVC) مورد...
Citations
... The work in [3] establishes the relation of residual and coding bits using Lagrangian optimization; therefore, the appropriate motion vector at the target bit-rate could be obtained. In [4], the Bayesian theorem and Markov chain are utilized to model the probability of modes for the transcoding between H.264/AVC and SVC. The work in [5] adjusts the discrete cosine transform coefficients to decrease the ones needed to be inverted and also to create a new method to measure the distortion in this transformation. ...
This paper presents a video bit-rate transcoder for baseline profile in H.264/AVC standard to fit the available channel bandwidth for the client when transmitting video bit-streams via communication channels. To maintain visual quality for low bit-rate video efficiently, this study analyzes the decoded information in the transcoder and proposes a Bayesian theorem-based region-of-interest (ROI) determination algorithm. In addition, a curve fitting scheme is employed to find the models of video bit-rate conversion. The transcoded video will conform to the target bit-rate by re-quantization according to our proposed models. After integrating the ROI detection method and the bit-rate transcoding models, the ROI-based transcoder allocates more coding bits to ROI regions and reduces the complexity of the re-encoding procedure for non-ROI regions. Hence, it not only keeps the coding quality but improves the efficiency of the video transcoding for low target bit-rates and makes the real-time transcoding more practical. Experimental results show that the proposed framework gets significantly better visual quality.
... Later, in 2011, the previous algorithm was adjusted for the baseline profile and P frames [26]. In 2012, Yeh et al. proposed another technique [27] for transcoding from H.264/AVC to SVC using probability models and Markov chains, and we presented another work [28,29] focusing on accelerating the mode decision algorithm, while our previous approaches focused only on motion estimation process. The present work is an extension of these last ones. ...
Nowadays, networks and terminals with diverse characteristics of bandwidth and capabilities coexist. To ensure a good quality of experience, this diverse environment demands adaptability of the video stream. In general, video contents are compressed to save storage capacity and to reduce the bandwidth required for its transmission. Therefore, if these compressed video streams were compressed using scalable video coding schemes, they would be able to adapt to those heterogeneous networks and a wide range of terminals. Since the majority of the multimedia contents are compressed using H.264/AVC, they cannot benefit from that scalability. This paper proposes a low-complexity algorithm to convert an H.264/AVC bitstream without scalability to scalable bitstreams with temporal scalability in baseline and main profiles by accelerating the mode decision task of the scalable video coding encoding stage using machine learning tools. The results show that when our technique is applied, the complexity is reduced by 87% while maintaining coding efficiency.
To support good video quality of experiences in heterogeneous environments, transcoding an existed HEVC (high efficiency video coding) video bitstream to a SHVC (scalability extension of HEVC) bitstream with quality scalability is highly required. A straightforward way is to first fully decode the input HEVC bitstream and then fully re-encode it with the SHVC encoder, which requires a tremendous computational complexity. To solve the problem, in this paper, a coding unit complexity (CUC)-based prediction method for predictions of CU (coding unit) depth and PU (prediction unit) mode for efficient HEVC-to-SHVC transcoding with quality scalability is proposed to significantly reduce the transcoding complexity. The proposed method contains two prediction techniques, including (i) early termination and (ii) adaptive confidence interval, and predicts the CU depth and PU mode relying on the decoded information from the input HEVC bitstream. Experimental results have shown that the proposed method significantly outperforms the traditional HEVC-to-SHVC method by 74.14% on average in reductions of encoding time for SHVC enhancement layer.
Over the last few decades multimedia usage has changed dramatically, with networks and terminals of diverse bandwidths and capabilities coexisting. To ensure a good quality of experience, this diverse environment requires adaptability of the video stream. If video streams were compressed using Scalable Video Coding (SVC) schemes, they would be able to adapt to these heterogeneous networks and a wide range of terminals. However, since the majority of multimedia contents are compressed using H.264/AVC, they cannot benefit from this scalability. This paper makes some proposals for improving a temporal H.264/AVC-to-SVC video transcoder so that it can convert an H.264/AVC bitstream without scalability to scalable bitstreams with temporal scalability. The idea behind the proposed transcoder is to accelerate interprediction, focusing on reducing the coding complexity of the mode decision and motion estimation tasks of the encoder stage by using information available after the H.264/AVC decoding stage. To accelerate the mode decision task, a decision tree for narrowing down the Macroblock (MB) types to be checked by the SVC encoder has been developed. On the other hand, to accelerate the motion estimation task, a reduced area is created dynamically for every MB and sub-MB. Moreover, time savings increase considerably when both proposals are adjusted to work together, as opposed to doing so in an isolated way. The results show that when our joint proposal is applied, the complexity is reduced by 98 % while maintaining coding efficiency.
This paper presents a video bitrate converter for baseline profile in H.264/AVC standards to control a selective coding scheme for several applications such as tactical scenes or multimedia area. Transmission channels have various capacities according to the application area, and the bitstream stored in computer should be converted in order not to exceed the capacities of a transmission channel. So the problem is how to convert compressed bitsreams of a given bit-rate into compressed bitsreams of other bit-rates. Such a specific transcoding problem in this paper is referred to as bit-rate conversion. Several researches have been done on bit-rate conversion for the bitstreams compressed by MPEG or H.264/AVC. But the existing schemes are not suitable for selective coding scheme because it needs to recover interest regions better image quality than background. So we propose a new bit-rate converter which considers the importance between interest regions and background.
Mobile digital TV environments demand flexible video compression like scalable video coding (SVC) because of varying bandwidths and devices. Since existing infrastructures highly rely on H.264/AVC video compression, network providers could adapt the current H.264/AVC encoded video to SVC. This adaptation needs to be done efficiently to reduce processing power and operational cost. This paper proposes two techniques to convert an H.264/AVC bitstream in Baseline (P-pictures based) and Main Profile (B-pictures based) without scalability to a scalable bitstream with temporal scalability as part of a framework for low-complexity video adaptation for digital TV broadcasting. Our approaches are based on accelerating the interprediction, focusing on reducing the coding complexity of mode decision and motion estimation tasks of the encoder stage by using information available after the H.264/AVC decoding stage. The results show that when our techniques are applied, the complexity is reduced by 98 % while maintaining coding efficiency.
