HE-AAC v2：数字媒体时代的高效音频编码技术

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"he-aac_v2 音频编码在当今数字媒体世界的应用" 在当今数字化媒体的世界里，高效音频编码（HE-AAC v2）扮演着至关重要的角色。作为国际标准，HE-AAC v2是音频压缩技术的里程碑，它在节省传输带宽的同时，确保了高质量的音频体验。本资源主要涵盖了HE-AAC v2编码的标准化过程、架构、功能特性、音频质量评估以及在各类产品和应用中的可用性。 1. 国际标准化：HE-AAC v2是MPEG-4标准的一部分，由国际组织进行规范和推广，确保其在全球范围内的兼容性和互操作性。这使得HE-AAC v2成为广播和流媒体服务的首选音频编码格式。 2. 架构：HE-AAC v2采用了先进的音频编码技术，包括立体声和多声道编码，最高支持48个声道，以满足各种应用场景的需求。其架构融合了AAC（Advanced Audio Coding）的核心优势，并引入了SBR（Spectral Band Replication）和PS（Parametric Stereo）等增强技术，实现了在低比特率下的高保真音频编码。 3. 功能性：HE-AAC v2的主要特性包括： - SBR技术：通过在高频部分复制低频信息，能够在较低比特率下保持宽广的频率响应，从而实现高质量的音频压缩。 - PS技术：参数立体声技术通过编码立体声信号的参数，而非原始信号，减少了数据量，尤其在单声道到立体声转换时，能有效节省带宽。 - 适应性：HE-AAC v2能够根据网络条件自适应调整编码质量，确保在不同环境下都能提供稳定且优质的音频体验。 4. 音频质量评估：经过多轮独立测试，HE-AAC v2被证实为全球最高效的音频压缩方案，即使在极低的比特率下，也能提供接近无损的音质。这一点使其在数字广播、移动通信和互联网流媒体等领域中广泛应用。 5. 应用与产品：HE-AAC v2编码家族已广泛应用于各种产品和应用程序中，如数字电视（例如DVB系统）、智能手机、音乐流媒体服务、在线视频平台等。其高效性和广泛的设备支持使得消费者能够在各种设备上享受高清晰度的音频内容。 HE-AAC v2是音频编码领域的杰出代表，它通过创新的技术手段，成功地平衡了音频质量和带宽需求，成为了现代数字媒体时代不可或缺的一部分。

AUDIO COMPRESSION

EBU TECHNICAL REVIEW – January 2006 3 / 12

S. Meltzer and G. Moser

HE-AAC-encoded audio data can exist in a variety of file formats with different extensions,

depending on the implementation and the usage scenario. Most commonly used file formats are the

MPEG-4 file formats MP4 and M4A, carrying the respective extensions .mp4 and .m4a. The “.m4a”

extension is used to emphasize the fact that a file contains audio only. The 3GP file format supports

all HE-AAC features for mono and stereo files up to a 48 kHz sampling rate. Additional file formats,

such as MPEG-2 and MPEG-4 ADTS are also available, along with others.

MPEG AAC

Research on perceptual audio codecs started about twenty years ago. Earlier research on the

human auditory system had revealed that hearing is mainly based on a short-term spectral analysis

of the audio signal. The so-called masking effect was observed: the human auditory system is not

able to perceive distortions that are masked by a stronger signal in the spectral neighbourhood.

Thus, when looking at the short-term spectrum, a so-called masking threshold can be calculated

for this spectrum. Distortions below this threshold are inaudible in the ideal case.

The goal is to calculate the masking threshold based on a psychoacoustic model and to process the

audio signal in a way that only audible information resides in the signal. Ideally, the distortion intro-

duced is exactly below the masking threshold and thus remains inaudible. Fig. 2 illustrates the

quantization noise produced by an ideal perceptual coding process.

If the compression rate is further increased, the distortion introduced by the codec violates the

masking threshold and produces audible artefacts (Fig. 3).

The main method of overcoming this problem in traditional perceptual waveform codecs is to limit

the audio bandwidth. As a consequence, more information is available for the remainder of the

spectrum, resulting in a clean but dull-sounding signal. Another method, called intensity stereo, can

only be used for stereo signals. In intensity stereo, only one channel and some panning information

is transmitted, instead of a left and a right channel. However, this is only of limited use in increasing

the compression efficiency as, in many cases, the stereo image of the audio signal gets destroyed.

At this stage, research on classical perceptual audio coding had reached its limits, as the hitherto

known methods did not seem to provide more potential to further increase coding efficiency. Hence,

a shift in paradigm was needed, represented by the idea that different elements of an audio signal,

such as spectral components or the stereo image, deserve different tools if they are to be coded

more efficiently. This idea led to the development of the enhancement tools, Spectral Band Replica-

tion and Parametric Stereo.

Spectral Band Replication

In traditional audio coding, a significant amount of information is spent in coding the high frequen-

cies, although the psychoacoustic importance of the last one or two octaves is relatively low. This

Masking Threshold

Energy

Frequency

Signal Energy

Quantization Noise

Introduced by an ideal perceptual codec

Quantisation Noise

violating the

Masking Threshold

Energy

Frequency

Figure 2

Inaudible quantization noise produced by an

ideal perceptual coding process

Figure 3

Waveform coding going beyond its limits:

audible artefacts appear above the masking

threshold

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