Lossless comparsion

本文对比了多种无损音频格式的特性,包括FLAC、WavPack、TAK、Monkey's Audio等。从编码速度、解码速度、压缩率等方面进行了详细比较,并介绍了各格式的灵活性、错误处理能力及软硬件支持情况。

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http://wiki.hydrogenaudio.org/index.php?title=Lossless_comparison

 

Comparison Table

Features FLAC WavPack TAK Monkey's OptimFROG ALAC WMA
Encoding speed fast very fast very fast fast slow average average
Decoding speed very fast very fast very fast average average fast average
Compression* 58.70% 58.0% 57.0% 55.50% 54.70% 58.50% 56.30%
Flexibility** very good very good very good very good very good bad bad
 
Error handling yes yes yes no yes   yes
Seeking yes yes yes yes yes yes yes
Tagging Vorbis tags ID3/APE APEv2 (exp.) ID3/APE ID3/APE Quicktime ASF
Hardware support very good limited no limited no good limited
Software support very good good average good average bad good
Hybrid/lossy no yes no no yes no no
Replay Gain yes yes yes no yes sort of no
RIFF chunks yes yes   yes yes   no
Streaming yes yes yes no yes yes yes
Pipe support yes yes yes yes yes no yes
Open source yes yes no yes no yes (decoding) no
Multichannel yes yes no no no yes yes
High resolution yes yes yes yes yes yes yes
OS support All All Win/Linux Wine All Win/Mac/Linux Win/Mac Win/Mac

(table continued below)

Features Shorten LA TTA LPAC MPEG-4 ALS MPEG-4 SLS Real Lossless
Encoding speed very fast slow very fast average average slow slow
Decoding speed very fast slow fast fast fast slow fast
Compression* 63.50% 53.50% 57.10% 57.20% 57.10%  ? 57.0%
Flexibility** bad average bad bad very good bad bad
 
Error handling no no yes no yes yes  
Seeking yes yes yes slow yes yes yes
Tagging no ID3v1 ID3 no yes yes Proprietary
Hardware support limited no limited no no no no
Software support very good bad average average bad bad bad
Hybrid/lossy no no no no no yes no
Replay Gain no no yes no yes yes no
RIFF chunks yes yes no yes      
Streaming no   no no yes yes yes
Pipe support yes yes no       no
Open source yes no yes no yes yes no
Multichannel no no yes no yes yes no
High resolution no no yes yes yes yes no
OS support All Win/Linux All Win/Linux/Sol All All Win/Mac/Linux
### 无损缩放技术在图像和音频处理中的应用 #### 图像的无损缩放 对于图像而言,无损缩放指的是放大或缩小图片尺寸而不损失任何原始数据的技术。一种实现方式是通过多网格反投影超分辨率方法[^1]。这种方法能够有效地恢复高分辨率图像细节,在保持原有质量的同时扩大图像规模。 此外,还有基于插值算法的方式来进行无损缩放操作。常见的有最近邻域法、双线性插值以及三次卷积插值等。这些方法可以在一定程度上保留图像特征,但在极端情况下可能会引入伪影或其他失真现象。 ```python import cv2 import numpy as np def lossless_image_scaling(image_path, scale_factor): img = cv2.imread(image_path) height, width = img.shape[:2] # 使用OpenCV库进行无损缩放 resized_img = cv2.resize(img, None, fx=scale_factor, fy=scale_factor, interpolation=cv2.INTER_NEAREST) return resized_img ``` #### 音频的无损缩放 关于音频信号的无损缩放,则涉及到改变播放速度而不会影响音调的变化过程。这通常采用时间拉伸(Time-Stretching)技术和移调(Pitch Shifting)来完成。其中,相位 vocoder 是一种广泛应用的时间频率表示模型,它允许独立调整时间和频率参数从而达到理想的缩放效果。 Python 中可以利用 `librosa` 库轻松地执行这样的变换: ```python import libroso def time_stretch(y, rate): y_stretched = librosa.effects.time_stretch(y, rate) return y_stretched ```
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