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    • 22. 发明授权
    • Watermarking with random zero-mean patches for printer tracking
    • 水印与随机的零均值补丁打印机跟踪
    • US06556688B1
    • 2003-04-29
    • US09268766
    • 1999-03-15
    • Viresh Ratnakar
    • Viresh Ratnakar
    • G06K900
    • G06T1/005G06T2201/0065
    • A steganographic method embeds hidden information like digital watermarks and digital fingerprints into an image by applying one or more zero-mean patches to a digital signal that conveys the image. Each zero-mean patch comprises elements having an average value substantially equal to zero. A printer tracking system is implemented by selecting a plurality of zero-mean patches according to a string of bits representing a printer serial number, and modifying printer control signals to embed the plurality of patches in each of a number of virtual cells within the image to be printed. The offset of the virtual cells with respect to the boundaries of the image and the location of the patches within each virtual cell are established in pseudo-random fashion. The printer tracking system further includes a technique for subsequently detecting the printer serial number by scanning the printed image and running a detection algorithm on the scanned data to recover the string of bits embedded in the zero-mean patches.
    • 隐写方法通过对传送图像的数字信号应用一个或多个零均值来将诸如数字水印和数字指纹的隐藏信息嵌入到图像中。 每个零均值包括具有基本上等于零的平均值的元件。 通过根据表示打印机序列号的比特串来选择多个零均值补丁来实现打印机跟踪系统,并且修改打印机控制信号以将多个补丁嵌入到图像内的多个虚拟单元的每一个中 打印。 以伪随机方式建立虚拟单元相对于图像的边界的偏移量以及每个虚拟单元内的区块的位置。 打印机跟踪系统还包括一种用于随后通过扫描打印图像并对扫描数据运行检测算法来检测打印机序列号以恢复嵌入在零均值中的比特串的技术。
    • 24. 发明授权
    • Image transformations in the compressed domain
    • 压缩域中的图像变换
    • US06298166B1
    • 2001-10-02
    • US09052039
    • 1998-03-30
    • Viresh RatnakarVictor IvashinVasudev Bhaskaran
    • Viresh RatnakarVictor IvashinVasudev Bhaskaran
    • G06K936
    • H04N19/48G06T3/60H04N19/90
    • Image processing techniques which involve direct manipulation of the compressed domain representation of an image to achieve the desired spatial domain processing without having to go through a complete decompression and compression process. The techniques include processing approaches for performing the eight operations in D4 (the dihedral group of symmetries of a square) on JPEG images using the discrete cosine transform (DCT) domain representation of the images directly. For a task such as image rotation by 90° (an operation in D4), DCT-domain based methods can yield nearly a five-fold increase in speed over a spatial-domain based technique. These simple compressed-domain based processing techniques are well suited to the imaging tasks that are needed in a JPEG-based digital still-camera system.
    • 图像处理技术涉及直接操纵图像的压缩域表示以实现期望的空间域处理,而不必经历完全的解压缩和压缩处理。 这些技术包括使用图像的离散余弦变换(DCT)域表示直接在JPEG图像上执行D4中的八个操作(平方对称的二面体组)的处理方法。 对于诸如图像旋转90°(D4中的操作)的任务,基于DCT域的方法可以在基于空间域的技术上产生几乎五倍的速度增加。 这些简单的基于压缩域的处理技术非常适合于基于JPEG的数码相机系统中所需的成像任务。
    • 25. 发明授权
    • Two row buffer image compression (TROBIC)
    • 两行缓冲图像压缩(TROBIC)
    • US06256415B1
    • 2001-07-03
    • US09095806
    • 1998-06-10
    • Viresh Ratnakar
    • Viresh Ratnakar
    • G06K936
    • G06T9/007
    • A Two Row Buffer Image Compression (TROBIC) technique is provided that operates on 32×2 blocks of pixels and uses a block-based coding scheme to provide alternative coding modes for image blocks containing different types of image data. The TROBIC algorithm automatically evaluates the image presented to determine regions containing text, graphics, synthetic images and natural images along with determining the most effective combination of global-index coding, local-index coding, lossless coding and wavelet coding to use to guarantee the desired compression ratio and high quality. The wavelet coding technique, in addition to being part of TROBIC, may be used independently in a wide variety of compression applications. The wavelet coding technique is carried out by a simple block codec with wavelets (SBCW) that uses wavelet filters to transform 32×2 image blocks into subband coefficients organized in a tree structure. These coefficients are then encoded using a novel entropy coding technique with precise rate control.
    • 提供了两行缓冲图像压缩(TROBIC)技术,其操作在32×2像素块上,并且使用基于块的编码方案为包含不同类型的图像数据的图像块提供替代编码模式。 TROBIC算法自动评估呈现的图像,以确定包含文本,图形,合成图像和自然图像的区域,同时确定全局索引编码,局部索引编码,无损编码和小波编码最有效的组合,以保证所需的 压缩比和高品质。 除了作为TROBIC的一部分之外,小波编码技术可以在各种各样的压缩应用中独立使用。 小波编码技术通过使用小波滤波器将32x2图像块变换为以树结构组织的子带系数的小波(SBCW)进行。 然后使用具有精确速率控制的新颖的熵编码技术对这些系数进行编码。
    • 26. 发明授权
    • Runs of adaptive pixel patterns (RAPP) for lossless image compression
    • 运行自适应像素图案(RAPP)用于无损图像压缩
    • US6038346A
    • 2000-03-14
    • US15408
    • 1998-01-29
    • Viresh Ratnakar
    • Viresh Ratnakar
    • H04N7/32G06T9/00H03M7/30H04N1/41H04N1/64G06K9/36H03M7/34H04N1/417
    • G06T9/005H04N1/64
    • A new lossless compression technique is provided that is specifically designed for palettized synthetic images which typically consist of several sharply delineated uniform-color regions with overlaid text and symbols. The associated algorithm uses patterns of previously-coded neighboring pixels to predict and code each pixel. Preferably, the four immediate and previously-coded neighbors of the current pixel are used. That is, the pixel immediately to the left (or west), the pixel immediately above and diagonally to the left (or northwest), the pixel immediately above (or north), and the pixel immediately above and diagonally to the right (or northeast). The basic pattern determined by the neighboring pixels is augmented by incorporating the number of times the value of each of the neighboring pixels is repeated in the corresponding direction. The prediction rules for different patterns are learned and updated from the image itself during the coding and decoding processes.
    • 提供了一种新的无损压缩技术,它是专门设计用于调色合成图像的,它通常由几个具有重叠文本和符号的锐利划分的均匀颜色区域组成。 相关联的算法使用先前编码的相邻像素的模式来预测和编码每个像素。 优选地,使用当前像素的四个立即和先前编码的邻居。 也就是说,紧邻左(或西)的像素,紧邻上方和对角线(或西北)的像素,紧邻上方(或北)的像素,以及右上方的对象(或东北部) )。 由相邻像素确定的基本图案通过并入每个相邻像素的值在相应方向上重复的次数来增加。 在编码和解码过程中,从图像本身学习和更新不同模式的预测规则。