When this happens, mode decision process is early terminated and encoding speed is greatly increased with small average PSNR loss.
The process terminates when the accumulated evidence, termed the decision variable, reaches ± B, the upper and lower decision bounds.
The process terminates when no new node is activated.
The process terminates when no more improvement can be made.
Previous studies indicate that neural activity in the lateral intraparietal area (LIP) represents the gradual accumulation of evidence toward a threshold level, or evidence bound, which terminates the decision process.
This boundary represents a suspension of the accumulation process but, unlike the decision bound in the SPRT and related models (Barnard, 1946; Wald, 1947; Good, 1979; Link, 1992; Smith and Ratcliff, 2004; Bogacz et al., 2006; Gold and Shadlen, 2007), does not terminate the decision process.
This boundary can be set to a pre-defined value to terminate the decision process while emphasizing either speed or accuracy at the expense of the other, or possibly balancing the two in the service of maximizing related quantities like reward rate (Gold and Shadlen, 2002; Palmer et al., 2005; Bogacz et al., 2006, 2010; Simen et al., 2009).
A fast CU partitioning algorithm is provided in Ref. [22] for HEVC, and this method early terminates the CU size decision process based on the Bayesian decision rule.
The decision process progressed erratically.
"There is a continually changing decision process.
Is the decision process transparent?
