if (srcImage.data != NULL) { vector<int>compression_params; compression_params.push_back(IMWRITE_JPEG_QUALITY); compression_params.push_back(quality); //图像压缩参数,该参数取值范围为0-100,数值越高,图像质量越高 bool bRet = imwrite(outputFileName, srcImage, compression_params); return bRet; ...
基于DCT的JPEG图像压缩算法的python代码 dct图像压缩 matlab 第四卷 VOL19 第18期 No.18 电子设计工程 Electronic Design Engineenng 20H年9月 Sep. 2011 基于DCT的图像压缩AMATLAB实现 (贵州大学理学院,贵州贵阳55闐25) 摘要:介绍JPEG图像压缩算法,并在MATLAB数学分析工具环境下从实验角度出发,较为直观地探讨了DC...
Repository files navigation README MIT license DCT-Image-Steganography For details on the theoretical foundations behind this Python application, please read the project report in the repo. "zigzag.py" location source: https://github.com/amzhang1/simple-JPEG-compression/blob/master/zigzag.pyAbout...
Implementation of Image compression using DCT jpegrledct-coefficientsrun-length-encodingimage-compressiondiscrete-cosine-transformdctjpeg-image-compression UpdatedOct 5, 2018 Python earthat/Digital-Image-Watermarking-Using-Optimized-DWT-DCT Star53
In a python framework, the project has been tested and a compressed picture was finally created. We were able to achieve 96.11%, 46.61%, and 67.91% compression in DCT, LZW, and Huffman, respectively.Sruthi, Jamandlamudi SravyaNational Institute of Technology RaipurJanghel, Rekh Ram...
def get_kurtosis(Im_in, kernel_size=(5, 5)): # get the kurtosis feature map of input image Im_in pad_width = ((kernel_size[0] // 2, kernel_size[0] // 2), (kernel_size[1] // 2, kernel_size[1] // 2)) # 计算 Kurtosis Feature Map kfm = np.zeros_like(Im_in) for i...
histogram cvhistogram matlab histogram excel histogram python histogram sas histogram image histogram 学习看histogram 文档格式: .pdf 文档大小: 214.46K 文档页数: 6页 顶/踩数: 0/0 收藏人数: 0 评论次数: 0 文档热度: 文档分类: 幼儿/小学教育--小学考试 ...
2. 可以减小图像的大小,节省存储空间和传输带宽。 3. 可以实现高效的图像压缩算法,如JPEG和MPEG等。 4. 由于DCT是线性变换,因此易于实现和优化。 总之,DCT实现是一种有效的图像压缩方法,广泛应用于多媒体通信、网络传输和存储等领域。Implementation of Image compression using DCT点...
gray2 = cv2.cvtColor(image2, cv2.COLOR_BGR2GRAY)# 将灰度图转为浮点型, 再进行dct变换dct1 = cv2.dct(np.float32(gray1)) dct2 = cv2.dct(np.float32(gray2))# 取左上角的8 * 8, 这些代表图片的最低频率# 这个操作等价于c++中利用opencv实现的掩码操作# 在python中进行掩码操作, 可以直接这样...
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