图像dct变换matlab代码-JPEG-Compressor-MATLAB-App:JPEG-Compressor-MATLA

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图像dct变换matlab代码一个MATLAB应用程序,可以使用图像的离散余弦变换矩阵压缩图像。 离散余弦变换(DCT)表示以不同频率振荡的余弦函数之和表示数据点的有限序列。 DCT由Nasir Ahmed于1972年首次提出,是在信号处理和数据压缩中广泛使用的转换技术。 它用于大多数数字媒体,包括数字图像(例如JPEG和HEIF,可以丢弃小的高频分量),数字视频(例如MPEG和H.26x),数字音频(例如杜比数字,MP3)和AAC),数字电视(例如SDTV,HDTV和VOD),数字广播(例如AAC +和DAB +)和语音编码(例如AAC-LD,Siren和Opus)。 DCT对科学和工程学中的许多其他应用也很重要,例如数字信号处理,通信设备,减少网络带宽的使用以及偏微分方程数值解的频谱方法。 使用余弦而不是正弦函数对压缩至关重要,因为事实证明(如下所述),需要较少的余弦函数来逼近典型信号,而对于微分方程,余弦表示边界条件的特定选择。 特别地,DCT是类似于离散傅立叶变换(DFT)的与傅立叶相关的变换,但是仅使用实数。 DCT通常与周期性且对称扩展的序列的傅里叶级数系数​​有关,而DF
JPEG-Compressor-MATLAB-App-master.zip
  • JPEG-Compressor-MATLAB-App-master
  • Output Interface.jpeg
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  • Image Compressor.mlappinstall
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  • appGRB.mlapp
    42.4KB
  • Main_Code.m
    8.4KB
  • README.md
    3.2KB
内容介绍
A MATLAB app which can compress an image with Discrete Cosine Transform Matrix of Image. A discrete cosine transform (DCT) expresses a finite sequence of data points in terms of a sum of cosine functions oscillating at different frequencies. The DCT, first proposed by Nasir Ahmed in 1972, is a widely used transformation technique in signal processing and data compression. It is used in most digital media, including digital images (such as JPEG and HEIF, where small high-frequency components can be discarded), digital video (such as MPEG and H.26x), digital audio (such as Dolby Digital, MP3 and AAC), digital television (such as SDTV, HDTV and VOD), digital radio (such as AAC+ and DAB+), and speech coding (such as AAC-LD, Siren and Opus). DCTs are also important to numerous other applications in science and engineering, such as digital signal processing, communications devices, reducing network bandwidth usage, and spectral methods for the numerical solution of partial differential equations. The use of cosine rather than sine functions is critical for compression, since it turns out (as described below) that fewer cosine functions are needed to approximate a typical signal, whereas for differential equations the cosines express a particular choice of boundary conditions. In particular, a DCT is a Fourier-related transform similar to the discrete Fourier transform (DFT), but using only real numbers. The DCTs are generally related to Fourier Series coefficients of a periodically and symmetrically extended sequence whereas DFTs are related to Fourier Series coefficients of a periodically extended sequence. DCTs are equivalent to DFTs of roughly twice the length, operating on real data with even symmetry (since the Fourier transform of a real and even function is real and even), whereas in some variants the input and/or output data are shifted by half a sample. There are eight standard DCT variants, of which four are common. The most common variant of discrete cosine transform is the type-II DCT, which is often called simply "the DCT". This was the original DCT as first proposed by Ahmed. Its inverse, the type-III DCT, is correspondingly often called simply "the inverse DCT" or "the IDCT". Two related transforms are the discrete sine transform (DST), which is equivalent to a DFT of real and odd functions, and the modified discrete cosine transform (MDCT), which is based on a DCT of overlapping data. Multidimensional DCTs (MD DCTs) are developed to extend the concept of DCT on MD signals. There are several algorithms to compute MD DCT. A variety of fast algorithms have been developed to reduce the computational complexity of implementing DCT. One of these is the integer DCT[1] (IntDCT), an integer approximation of the standard DCT,[2] used in several ISO/IEC and ITU-T international standards.[2][1] DCT compression, also known as block compression, compresses data in sets of discrete DCT blocks.[3] DCT blocks can have a number of sizes, including 8x8 pixels for the standard DCT, and varied integer DCT sizes between 4x4 and 32x32 pixels.[1][4] The DCT has a strong "energy compaction" property,[5][6] capable of achieving high quality at high data compression ratios.[7][8] However, blocky compression artifacts can appear when heavy DCT compression is applied.
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