ACO：基于自适应编码顺序的无损质量分数压缩

 马光明

Li Fu

 刘先明

 石光明

 研究文章

ACO：基于自适应编码顺序的无损质量分数压缩

Background

 声明

挖掘更多相关信息

Methods

沿着最相对的方向遍历质量得分

 结果和讨论

Algorithm 1:ACO algorithm framework
Input: A FASTQ file.
Output: The compressed quality score file
    STEP1: Data preprocessing
    1. Use a \(N \times K\) matrix \(\mathbf{Q}\) to store the quality score of
    FASTQ file.
    2.Use a \(N \times K\) matrix \(\mathbf{P}\) to store the base value of FASTQ
    file.
    for all \(0 \leq n \leq N\) do
        for all \(0 \leq k \leq K\) do
            calculate the max of \(\mathbf{Q}(n, k)\) as symbol_max
            calculate the \(\min\) of \(\mathbf{Q}(n, k)\) as symbol_min
            symbol_number=symbol_max-symbol_min+1
        end for
    end for
    STEP2: Composite context model
    1. Calculate model_num by model_num = symbol_number \({ }^{3}\).
    2. Use a \(N \times 1\) vector \(\mathbf{M}\) to store the mean value of the quality
    value in each line.
    for all \(0 \leq n \leq N\) do
        for all \(0 \leq k \leq K\) do
            \(A=\mathbf{M}(n, 1)\)
            \(B=\max [\mathbf{Q}(n, k-1), \mathbf{Q}(n, k-2)]\)
            \(C=\max [\mathbf{Q}(n, k-3), \mathbf{Q}(n, k-4)]\)
            if \(\quad([\mathbf{Q}(n, k-3)==\mathbf{Q}(n, k-4)]) \quad D=0\);
            else \(\quad D=1\)
            \(E=[\mathbf{P}(n, k), \mathbf{P}(n, k-1)]\)
            model_ \(i d x=[A, B, C, D, E]\)
        end for
    end for
    STEP3: Snake traversal coding
    \(\mathrm{i}=0\);
    for all \(0 \leq k \leq K\) do
        for all \(0 \leq n \leq N\) do
        if \((k \% 2==0) \quad i=k\)
        else \(\quad i=N-n-1\);
        use arithmetic encoder to compress \(\mathbf{Q}(n, k)\) with model_idx
        end for
    end for

结论和未来工作

 缩略语

 致谢

Funding

数据和材料的可用性

 竞争利益

 同意出版

 参考资料

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