Abstract
Skillful rate-control inside motion estimation (ME) plays a crucial part in real-time mobile video coding because the available transmission bandwidth in mobile video coding applications within handhold devices is time-varying instead of fixed in the video coding standard H.264/AVC or H.265/high efficiency video coding (HEVC). Rate control consists of coding bit allocation and quantization parameter selection, which are difficult to implement in hardware. To overcome this issue, this paper achieves an ME rate control design on the basis of the improved machine learning (ML) scheme, and the auxiliary experimental outcomes reveal the coding effectiveness and feasibility of the proposed method.
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