An iterative hillclimbing algorithm for discrete optimization on images: Application to joint encoding of image transform coefficients

Piya Bunyaratavej, David J. Miller

    Research output: Contribution to journalArticle

    3 Citations (Scopus)

    Abstract

    We develop an iterative, hillclimbing-based assignment algorithm for the approximate solution of discrete-parameter cost minimization problems defined on the pixel sites of an image. While the method is applicable to a number of problems including encoding, decoding, and segmentation, this letter focuses on entropy-constrained encoding. For typical statistical image models, the globally optimal solution requires an intractable exhaustive search, while standard greedy methods, though tractable in computation, may be quite suboptimal. Alternatively, our method is guaranteed to perform no worse (and typically performs significantly better) than greedy encoding, yet with manageable increases in complexity. The new approach uses dynamic programming as a local optimization "step," repeatedly applied to the rows (or columns) of the image, until convergence. For a DCT framework, with entropy-constrained TCQ applied to the coefficient sources, the new method gains as much as 0.8 dB over standard greedy encoding.

    Original languageEnglish (US)
    Pages (from-to)46-50
    Number of pages5
    JournalIEEE Signal Processing Letters
    Volume9
    Issue number2
    DOIs
    StatePublished - Feb 1 2002

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    Hill Climbing
    Discrete Optimization
    Encoding
    Entropy
    Transform
    Coefficient
    Dynamic programming
    Decoding
    Pixels
    Cost Minimization
    Image Model
    Local Optimization
    Exhaustive Search
    Minimization Problem
    Statistical Model
    Dynamic Programming
    Costs
    Approximate Solution
    Assignment
    Segmentation

    All Science Journal Classification (ASJC) codes

    • Signal Processing
    • Electrical and Electronic Engineering
    • Applied Mathematics

    Cite this

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