An adaptive, feature-based low power motion estimation algorithm

Gupte, Ajit; Bharadwaj, Aprameya

doi:10.1109/icme.2008.4607609

Cited by 2 publications

(4 citation statements)

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“…In Ref. [25], we introduced feature based partitioning using Hadamard transform. In this section, we begin with explaining the motivation behind adaptive partitioning based on MB features with an example.…”

Section: Feature Based Macro-block Partitioningmentioning

confidence: 99%

“…We then briefly describe the algorithm in Ref. [25] and its limitations before proposing a new method based on hierarchical partitioning approach based on analysis of both macro-block features and variance. Figure 4 provides an illustration of the idea using macro-block containing vertical stripes.…”

Section: Feature Based Macro-block Partitioningmentioning

confidence: 99%

“…In Ref. [25], spatial frequency components were estimated using Hadamard transform of 4 × 4 sub-block mean values and the dominant transform coefficients were used to select the partitioning for the MB. Different partitioning options were chosen based on the dominant Hadamard coefficients.…”

Section: Adaptive Partitioning Based On 4 × 4 Hadamardmentioning

confidence: 99%

“…The cost calculations are based on the method described in Ref. [25]. The different threshold parameters for both the algorithms were adjusted so that the average bit rate difference between the two algorithms is very small.…”

Section: Hierarchical Partitioning Approach (Hp-age)mentioning

confidence: 99%

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Adaptive Global Elimination Algorithm for Low Power Motion Estimation<BR> (Journal of Low Power Electronics, Vol. 5, pp. 1–16 (2009))

Gupte¹,

Bharadwaj²

2009

Journal of Low Power Electronics

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Motion estimation typically consumes 50% to 70% of total power in video encode application. Optimizing the power consumption of motion estimation process is of great importance to low power video applications. Power dissipation increases with computational complexity. Reduction in motion estimation complexity is usually associated with increase in bit rate and a loss of quality. We explore a set of algorithms that reduce the complexity of motion estimation by adaptively changing the matching complexity based on macro-block features yet have only a modest cost in terms of bit rate increase and quality loss. The adaptive techniques are applied to the global elimination algorithm, which is a well known motion estimation algorithm. The global elimination algorithm uses fixed partition sizes and shapes irrespective of the nature of the macro-block. We show that by adapting the partition sizes and shapes according to the macro-block features such as variance and Hadamard coefficients, the computational complexity of global elimination algorithm can be significantly reduced with only a small increase in bit rate. We also propose a novel center-biased search order that uses early termination method designed to work with the global elimination algorithm. The adaptive match and center-biased search together result in around 57% reduction in computational complexity and 50% reduction in power dissipation compared to the original global elimination algorithm.

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Section: Feature Based Macro-block Partitioningmentioning

confidence: 99%

Section: Feature Based Macro-block Partitioningmentioning

confidence: 99%

Section: Adaptive Partitioning Based On 4 × 4 Hadamardmentioning

confidence: 99%

Section: Hierarchical Partitioning Approach (Hp-age)mentioning

confidence: 99%

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