Question

JCTVC-G763说：＆＃34;这个新表是使用定义每个州LPS范围的表格得出的。在推导出这个新表时，假设CABAC引擎中的范围寄存器取值R，其概率与1 / R＆＃34;成比例。怎么理解呢？如何生成数组m_entropyBits？

const Int ContextModel::m_entropyBits[ ContextModel::m_totalStates ] =
{

#ifdef FAST_BIT_EST
  // Corrected table, most notably for last state

  0x07b23, 0x085f9, 0x074a0, 0x08cbc, 0x06ee4, 0x09354, 0x067f4, 0x09c1b, 0x060b0, 0x0a62a, 0x05a9c, 0x0af5b, 0x0548d, 0x0b955, 0x04f56, 0x0c2a9,
  0x04a87, 0x0cbf7, 0x045d6, 0x0d5c3, 0x04144, 0x0e01b, 0x03d88, 0x0e937, 0x039e0, 0x0f2cd, 0x03663, 0x0fc9e, 0x03347, 0x10600, 0x03050, 0x10f95,
  0x02d4d, 0x11a02, 0x02ad3, 0x12333, 0x0286e, 0x12cad, 0x02604, 0x136df, 0x02425, 0x13f48, 0x021f4, 0x149c4, 0x0203e, 0x1527b, 0x01e4d, 0x15d00,
  0x01c99, 0x166de, 0x01b18, 0x17017, 0x019a5, 0x17988, 0x01841, 0x18327, 0x016df, 0x18d50, 0x015d9, 0x19547, 0x0147c, 0x1a083, 0x0138e, 0x1a8a3,
  0x01251, 0x1b418, 0x01166, 0x1bd27, 0x01068, 0x1c77b, 0x00f7f, 0x1d18e, 0x00eda, 0x1d91a, 0x00e19, 0x1e254, 0x00d4f, 0x1ec9a, 0x00c90, 0x1f6e0,
  0x00c01, 0x1fef8, 0x00b5f, 0x208b1, 0x00ab6, 0x21362, 0x00a15, 0x21e46, 0x00988, 0x2285d, 0x00934, 0x22ea8, 0x008a8, 0x239b2, 0x0081d, 0x24577,
  0x007c9, 0x24ce6, 0x00763, 0x25663, 0x00710, 0x25e8f, 0x006a0, 0x26a26, 0x00672, 0x26f23, 0x005e8, 0x27ef8, 0x005ba, 0x284b5, 0x0055e, 0x29057,
  0x0050c, 0x29bab, 0x004c1, 0x2a674, 0x004a7, 0x2aa5e, 0x0046f, 0x2b32f, 0x0041f, 0x2c0ad, 0x003e7, 0x2ca8d, 0x003ba, 0x2d323, 0x0010c, 0x3bfbb

#endif
};

Answer 1

你的最终目标是什么？是要了解CABAC上下文表还是仅在编码时打印出费率？

JCTVC-G763 CE1：CABAC的基于表的比特估计

1 个答案: