Computation-constrained dynamic search range control for real-time video encoder

Ji, Xianghu; Jia, Huizhu; Liu, Jie; Xie, Xiaodong; Gao, Wen

doi:10.1016/j.image.2014.12.002

Cited by 3 publications

(1 citation statement)

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“…Ji et al [25] modeled a deviation metric of the motion vector predictor to predict the relationship between the size of the SA and the difference of the motion vector. In Pakdaman et al [26] and Singh et al [27] the authors propose a new fast ME algorithm to reduce the number of search points in the ME process.…”

Section: Dynamic Search Rangementioning

confidence: 99%

Low-energy motion estimation memory system with dynamic management

Silveira

Amaral

Povala

et al. 2021

J Real-Time Image Proc

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The digital video coding process imposes severe pressure on memory traffic, leading to considerable power consumption related to frequent DRAM accesses. External off-chip memory demand needs to be minimized by clever architecture/ algorithm co-design, thus saving energy and extending battery lifetime during video encoding. To exploit temporal redundancies among neighboring frames, the motion estimation (ME) algorithm searches for good matching between the current block and blocks within reference frames stored in external memory. To save energy during ME, this work performs memory accesses distribution analysis of the test zone search (TZS) ME algorithm and, based on this analysis, proposes both a multi-sector scratchpad memory design and dynamic management for the TZS memory access. Our dynamic memory management, called neighbor management, reduces both static consumption-by employing sectorlevel power gating-and dynamic consumption-by reducing the number of accesses for ME execution. Additionally, our dynamic management was integrated with two previously proposed solutions: a hardware reference frame compressor and the Level C data reuse scheme (using a scratchpad memory). This system achieves a memory energy consumption savings of 99.8% and, when compared to the baseline solution composed of a reference frame compressor and data reuse scheme, the memory energy consumption was reduced by 44.1% at a cost of just 0.35% loss in coding efficiency, on average. When compared with related works, our system presents better memory bandwidth/energy savings and coding efficiency results.

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Section: Dynamic Search Rangementioning

confidence: 99%