To acquire the optimal coding mode of each macroblock, the H.264/AVC encoder exhaustively calculates the ratedistortion
cost for all available modes and chooses the minimum one as the best mode. Therefore, the mode decision
process is very computationally demanding. To reduce the computation complexity of the rate-distortion cost, in this
paper, we propose a novel rate estimation model for the mode decision in H.264/AVC. By modeling the transform
coefficients with Generalized Gaussian distributions (GGD), a direct relationship between the magnitude and the
information bits of the quantized transform coefficients is deduced. Based on this deduction, the weighted sum of
quantized transform coefficients is proposed as an efficient bit-rate estimator of the residual blocks. Extensive
experiments show that the proposed algorithm can save up to 30% of total encoding time with ignorable degradation in
coding performance for both inter- and intra-mode decision.
Fine-Granular SNR scalable (FGS) technologies in H.264/AVC-based scalable video coding (SVC) provide a flexible
and effective foundation for scaling FGS enhancement layer (EL) to accommodate different and variable network
capacities. To support smooth quality extraction of SVC FGS videos, it's important to obtain the Rate-Distortion (R-D)
function of each picture or group of pictures (GOP). In this paper, firstly, we introduce the R-D analysis of SVC FGS
coding in our prior work. Then, with the analysis and models, we present virtual GOP concept and a virtual-GOP-based
packet scheduling algorithm is proposed to acquire the optimal packet scheduling sequence in a virtual GOP. Based on
the packet scheduling algorithm and the R-D analysis of FGS EL, an effective and flexible D-R model is proposed to
describe the D-R function of the virtual GOP. Then, with the R-D model of virtual GOPs, a practical non-search
algorithm for smooth quality reconstruction is introduced. Compared to the quality layer method, the reconstructed video
quality is improved not only objectively but also subjectively.
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