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  • Deb Mukherjee's avatar
    Modeling default coef probs with distribution · fd18d5df
    Deb Mukherjee authored
    Replaces the default tables for single coefficient magnitudes with
    those obtained from an appropriate distribution. The EOB node
    is left unchanged. The model is represeted as a 256-size codebook
    where the index corresponds to the probability of the Zero or the
    One node. Two variations are implemented corresponding to whether
    the Zero node or the One-node is used as the peg. The main advantage
    is that the default prob tables will become considerably smaller and
    manageable. Besides there is substantially less risk of over-fitting
    for a training set.
    Various distributions are tried and the one that gives the best
    results is the family of Generalized Gaussian distributions with
    shape parameter 0.75. The results are within about 0.2% of fully
    trained tables for the Zero peg variant, and within 0.1% of the
    One peg variant.
    The forward updates are optionally (controlled by a macro)
    model-based, i.e. restricted to only convey probabilities from the
    codebook. Backward updates can also be optionally (controlled by
    another macro) model-based, but is turned off by default. Currently
    model-based forward updates work about the same as unconstrained
    updates, but there is a drop in performance with backward-updates
    being model based.
    The model based approach also allows the probabilities for the key
    frames to be adjusted from the defaults based on the base_qindex of
    the frame. Currently the adjustment function is a placeholder that
    adjusts the prob of EOB and Zero node from the nominal one at higher
    quality (lower qindex) or lower quality (higher qindex) ends of the
    range. The rest of the probabilities are then derived based on the
    model from the adjusted prob of zero.
    Change-Id: Iae050f3cbcc6d8b3f204e8dc395ae47b3b2192c9