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This paper presents a strategy that efficiently allocates the limited resource and still maintains the coding performance for the spatial scalability of H.264/SVC. An integrated computation-scalable algorithm is proposed on the basis of priority-based mode decision (PBMD) and activitybased motion estimation (ABME). The rate-distortion cost (RD-cost) of base layer is utilized as the priority to decide the mode in the enhancement layer, and the motion vector difference (MVD) in the base layer is employed to determine the search range as well. Through the proposed approach, computation complexity of the enhancement layer could be reduced efficiently and well allocated. A fast algorithm with early termination mechanism is also proposed in this paper. Experimental results demonstrate that the various ratedistortion and time-saving performances with computation scalability are realized. [ABSTRACT FROM AUTHOR]

Highlights • Novel unit prediction method for efficient HEVC-to-SHVC transcoding is proposed. • Two proposed techniques are early termination and adaptive confidence interval. • We are among the first to propose a fast HEVC-to-SHVC transcoding framework. • Proposed method reduces encoding time of SHVC by 74.14% almost without quality loss. 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. [ABSTRACT FROM AUTHOR]

Scalable video coding incorporated with computation-aware ability achieves quality as well as being computation scalable. This paper presents a computation-aware algorithm for scalable video coding with spatial/quality scalability aiming for the best trade-off between rate distortion performance and computational consumption. We first observe and analyze and then establish a model for the motion vector difference relationship between the scalable base and enhancement layers. By using the modeling results, a linear algorithm for computation distribution is thus proposed to allocate the computation for each macroblock in the enhancement layer. In addition, the rate distortion costs of the base layer are also taken into account for the computation allocation process in order to further improve the coding performance. The simulation results demonstrate that our proposed computation-aware algorithm not only accomplishes better rate distortion performance than other works under the same computational constraints, but also achieves less computation necessities. [ABSTRACT FROM AUTHOR]