By Qifan Y.
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Extra info for A 2. 79 competitive online algorithm for two processor real-time systems with uniform value density
M1 and M2 call encoder routines in the minimization steps. By adding calls to decoder routines, we can compute the resulting distortion. We incorporate this idea into Algorithm RD. Algorithm RD minimizes a linear combination of rate and distortion. Let B ~v;~c denote the number of bits to code the current macroblock using motion vector ~v and coding decisions ~c. Similarly, let D~v;~c be the resulting mean squared error. 1 This objective function is very similar to the Lagrange cost function C presented 2 The transform coding of the chrominance blocks could be included as well.
6 where HR is the model for rate and HD is the model for distortion. 5 for both HR and HD, the combined heuristic function, H = HR + HD, would have the same form as HR and HD. Therefore, we can interpret the heuristic as modeling a combined rate-distortion function. 4. HEURISTIC ALGORITHMS 47 DCT Bits vs. MAD for Q=12 DCT Bits vs. MAD for Q=16 350 250 200 250 DCT Bits DCT Bits 300 200 150 150 100 100 50 50 5 5 10 15 20 25 30 35 MAD Prediction Error MAD Prediction Error DCT Bits vs. MAD for Q=20 DCT Bits vs.
Using the model, the encoder performs motion estimation to determine the motion that exists between a reference frame and the current frame. The reference frame can occur temporally before the current frame forward prediction or after the current frame backward prediction. An advanced technique, called bidirectional prediction, uses two reference frames, one each for forward and backward prediction, and interpolates the results. This usually gives better prediction and handles the case where an object is temporarily occluded.
A 2. 79 competitive online algorithm for two processor real-time systems with uniform value density by Qifan Y.