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AAC家族命名

2012年09月13日 ⁄ 综合 ⁄ 共 4088字 ⁄ 字号 评论关闭
AAC 家族之名称和算法名称
  
  AAC 经常让大家摸不到头脑,而且很多工具对 AAC 版本的叫法千奇百怪,甚至有些编码器/播放器甚至
  误导大家。例如 有些将 HE AAC 认作 AAC-LC, 其实也没有错,但是很不精确。 下面是一份对AAC家族相关
  叫法的一个明确:
  
   AAC = MPEG2 AAC ~= MP3 + TNS + TP (It is not an upgrade of MP3 since it is not backward compatible but uses all MP3's features in a better way).
  
  MPEG4 AAC = MPEG2 AAC + LTP + PNS
  There are several profliles depending on the decoding/encoding complexity, required power, delay, bandwith characteristics, error resilience characteristics, etc... The most used profile in the PC arena is the AAC LC (Low Complexity) = MPEG4 AAC without LTP.
  
  HE-AAC = SBR + AAC LC
   Coding Technologies, developers of SBR, named this coding aacPlus™, also known as AAC+, HE-AAC, AACP, AAC-LC+SBR, etc... SBR technology was prevously introduced in the MP3pro codec.
  
  HE-AAC v2= PS + HE-AAC
   Coding Technologies, developers of the MPEG Parametric Stereo, named this coding aacPlus™ v2 as a new revision of the previous release. It is also known as AAC++, EAAC+, Enhanced HE-AAC, EAACP, HE-AAC+PS, etc... Recently it was standarized by ISO as HE-AAC v2.
  
  S-AAC...(Just guessing, not yet released but in Reference Model 0 stage)
   Since MPEG is focusing in multichannel, the next standard will be something based in the Spatial Audio Coding tool standarized as MPEG Surround, that allows to do someting similar to PS but aimed to 5.1ch or 7.1ch content. This could be named as S-AAC, AAC Surround or AACS, Surround HE-AAC, [Put your favorite name here]. There isn't an official name for it yet.
  
  Terms and acronyms:
  
  AAC Advanced Audio Coding, developed by Dolby Laboratories.
  
   TNS Temporal Noise Shaping is a tool designed to control the location, in time, of the quantization noise by transmission of filtering coefficients.
  
  TP Temporal Prediction is a tool designed to enhance compressibility of stationnary signals.
  
   LTP Long Term Prediction is once again a prediction tool. This one requires less computation power but it is far more complex than the one used in MPEG-2 AAC, while providing comparable coding performance.
  
   PNS Perceptual Noise Substitution, allows to replace coding of noise-like parts of the signal by some noise generated on the decoder side, so the decoding result is not deterministic among multiple decoding processes of the same encoded data.
  
  SBR Spectral Band Replication is a tool that creates associated higher frequency content based on the lower frequencies and coding it as statistical information: level, distribution and ranges. Each of these parameters is encoded separately, taking account of their distinctive characteristics. It involves reconstruction of a noise-like frequency spectrum by employing a noise generator with some statistical information (level, distribution, ranges), so the decoding result is not deterministic among multiple decoding processes of the same encoded data. Both ideas are based on the principle that the human brain tends to consider high frequencies to be either harmonic phenomena associated with lower frequencies or noise, and is thus less sensitive to the exact content of high frequencies in audio signals.
  
  PS Parametric Stereo, the stereo image information is separated from the mono signal being represented as a small amount of high quality parametric stereo information. The scheme relies on dissecting the incoming audio signal into three ‘objects’ that are a common constituent of all audio signals: transients, sinusoids and noise The stereo information is efficiently parameterized. Each of these objects is encoded separately, taking account of their distinctive characteristics. Like PNS and SBR the decoding result is not deterministic among multiple decoding processes of the same encoded data.
  
  SAC Spatial Audio Coding exploits inter-channel differences in level, phase and coherence to capture the spatial image of a multi-channel audio signal relative to a transmitted downmix signal. It encodes each of these cues separately taking account of their distinctive characteristics such that the cues, and the transmitted signal, can be decoded to synthesize a high quality multi-channel representation allowing higher compression than separate channel coding.
  
  AAC 定点解码器之 算法复杂度分析
  
  [测试环境]: Sparc V8 CPU @ 128MHz, 无乘法器, 32K DCache.
  [测试向量]: AAC LC+SBR, FAAC编码
  [测试结果]:
  AAC decoder total: 00003c21b cycles
  requant: 000004225 cycles 6.8%
  spectral_data: 00000618b cycles 10.1%
  scalefactors: 000000a21 cycles 1.0%
  output: 000000000 cycles 0.0%
  FB: 000022dd0 cycles 58.0%
  MDCT[0256]: 000005fd5 cycles 9.9%
  CFFT[0064]: 0000047e6 cycles 7.4%
  MDCT[2048]: 000008663 cycles 13.9%
  CFFT[0512]: 0000091fd cycles 15.2%
  MDCT[1024]: 000000000 cycles 0.0%
  CFFT[0256]: 000000000 cycles 0.0%
  
  综上所述,FilterBank 是 SBR中最复杂的算法。

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