Objective

2

A fusion mutation strategy that considers global and local processing is proposed in this paper to solve the low acceptance probability

asymmetry of sampling quantity and illumination distribution in the uneven brightness area

and poor fluidity in the uneven brightness region of the multiplexed Metropolis light transport algorithm.

Method

2

The local pixel plane luminance uniformity is dynamically measured as a whole through variance

and the sampling step size is adaptively adjusted

that is

the number of samples per pixel location is recorded. The current sampled pixel is measured through variance

and the brightness uniformity in the eight neighborhoods and the sampling step size of the current Markov chain are adjusted by the calculation result of variance when the samples reaches the threshold of the current sampled pixel and the current sample is the first sample of the Markov chain or the first sample after the large mutation. Then

the eight-neighbor sampling weight is calculated by considering the number of samples and illumination when the number of samples of the current sampled pixel reaches the threshold and the current sample is a small mutation.

Result

2

The experiment is divided into two parts. The first part verifies the effectiveness of adjusting the sampling step size on the image distribution and selects the lengthened and shortened step size parameters in accordance with the relationship between the obtained local eight-neighbor luminance variance and the step size in the uniform or uneven brightness region. The optimal combination of parameters enables to adaptively adjust the sampling step size in accordance with the scene. Results show that the adaptively adjusting of sampling step size can effectively guide the samples to enhance the sampling in the uneven brightness region for improving the rendering of the details and the flow of samples in the uneven brightness region that cannot be perceived by the human eye to better traverse the pixel plane. The second part combines the sampling quantity and illumination distribution strategy with the adaptively adjusting of sampling step size strategy to form a fusion mutation strategy and is compared with the multiplexed Metropolis light transport algorithm

sampling quantity and illumination distribution strategy

and adaptively adjusting of sampling step size strategy under different lighting and material scenarios. Results show that the fusion mutation strategy algorithm uses the advantages of sampling quantity and illumination distribution and adaptively adjusting of sampling step size strategy. As shown in rendered resulting graphs

the fusion mutation algorithm renders the highlighting effect and makes the rendering result of the uneven brightness region exquisite. The fusion mutation algorithm is realistic in terms of detail performance. As shown in the obtained sampling density distribution maps

the fusion mutation algorithm enhances the sampling in the uneven brightness region

improves the mobility of samples in the uniform region

and verifies the effectiveness of adaptively adjusting of sampling step size strategy. For the evaluation of rendered data indicators

the fusion mutation strategy combined with the adaptively adjusting step strategy and the method of taking the sampling quantity and illumination distribution is compared with the original algorithm. The fusion mutation algorithm improves the indicators in a balanced manner on the basis of the rendering time between the adaptively adjusting of sampling step size and sampling quantity and illumination distribution strategies.

Conclusion

2

A fusion mutation strategy that combines the dynamically adjusting of sampling step size strategy

which is a global sampling strategy

and the enhanced highlight area with sampling and lighting effect strategy

which is a local sampling strategy

is proposed in this paper. On the one hand

the proposed method can help the Markov chain to shorten the sampling step size in areas

where the brightness is different and the acceptance probability is low

and modify the sampling weight in the intensively changing brightness region to improve the local sampling quantity and illumination distribution asymmetry. On the other hand

the Markov chain is easily trapped in the local where the brightness is uniform because of the constraint of the multiplexed Metropolis light transport(MMLT) algorithm. The proposed method lengthens the sampling step size to accelerate the flow of samples within the region to improve the ergodicity of the algorithm.