Compression is an important field of digital image processing where well-engineered methods with high performance exist. Partial differential equations (PDEs), however, have not much been explored in this context so far. In our paper we introduce a novel framework for image compression that makes use of the interpolation qualities of edge-enhancing diffusion. Although this anisotropic diffusion equation with a diffusion tensor was originally proposed for image denoising, we show that it outperforms many other PDEs when sparse scattered data must be interpolated. To exploit this property for image compression, we consider an adaptive triangulation method for removing less significant pixels from the image. The remaining points serve as scattered interpolation data for the diffusion process. They can be coded in a compact way that reflects the B-tree structure of the triangulation. We supplement the coding step with a number of amendments such as error threshold adaptation, diffusion-based point selection, and specific quantisation strategies. Our experiments illustrate the usefulness of each of these modifications. They demonstrate that for high compression rates, our PDE-based approach does not only give far better results than the widely-used JPEG standard, but can even come close to the quality of the highly optimised JPEG2000 codec.

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Image Compression with Anisotropic Diffusion

A signal adaptive filtering method for reducing blocking effect and ringing noise, a signal adaptive filter, and a computer readable medium. The signal adaptive filtering method capable of reducing blocking effect and ringing noise of image data when a frame is composed of blocks of a predetermined size includes the steps of: (a) generating blocking information for reducing the blocking effect and ringing information for reducing the ringing noise, from coefficients of predetermined pixels of the upper and left boundary regions of the data block when a frame obtained by deconstructing a bitstream image data for inverse quantization is an intraframe; and (b) adaptively filtering the image data passed through inverse quantization and inverse discrete cosine transform according to the generated blocking information and ringing information. Therefore, the blocking effect and ringing noise can be eliminated from the image restored from the block-based image, thereby enhancing the image restored from compression.

Signal adaptive filtering method, signal adaptive filter and computer readable medium for storing program therefor

Image signal processing has the steps of: judging from four pixels adjacent to an object pixel whether the object pixel constitutes a single edge; judging whether the single edge is horizontal or vertical, if it is judged that the object pixel constitutes the single edge; and interpolating the object pixel in accordance with at least right and left two pixels adjacent to the object pixel in a horizontal direction, if it is judged that the single edge is horizontal, and interpolating the object pixel in accordance with at least upper and lower two pixels adjacent to the object pixel in a vertical direction, if it is judged that the single edge is vertical.

Image signal interpolation

An original image signal, which represents an original image and are composed of original image signal components representing a plurality of sampling points, that are arrayed at predetermined intervals and in a lattice-like form, is obtained. A judgment is made as to whether an interpolation point belongs to an image edge portion, at which the change in the original image signal is sharp, or belongs to a flat portion, at which the change in the original image signal is unsharp. Interpolating operation processes, one of which is to be employed for the interpolation point, is changed over to each other in accordance with the results of the judgment. Interpolated image signal components corresponding to interpolation points are thereby obtained from the interpolating operation processes such that a visible image, in which a character pattern and an image edge portion are free from any step-like pattern and are sharp and a flat portion has an appropriate level of sharpness, can be reproduced from the interpolated image signal components.

Interpolating operation method and apparatus for image signals

The invention features a system and method for adding hyperlinked information to television broadcast system. The system includes a video source providing video information and an annotation system. The annotation system generates annotation data to be associated with the video information and generates annotation data timing information. The hyperlinked broadcast system also includes an augmented video information transmission generator that receives the annotation data, the video information, and the annotation data timing information. The augmented video information transmission generator generates an augmented video transmission signal including the annotation data, the annotation data timing information and the video information. In operation, the augmented video information transmission generator associates the video information with the annotation data using the annotation data timing information. A receiver displays the annotation information associated with the video signal on a frame by frame basis.

Method and apparatus for hyperlinking in a television broadcast

Edge or contour information is extracted from an image array by filtering and encoded. In order to improve reproduction accuracy, two separate filters (302, 304) are used to detect edge information. One of the filters detects 'sharp' edges, or edges in which the discontinuity in pixel intensity values occurs over a range of a few pixels. The other filter detects 'level' edges in which the pixel intensity value discontinuity occurs over a larger range of pixels than the 'sharp' edges. The 'smooth' areas between edges or contours are assumed to vary continuously between the contours, but for efficient implementation, a one-dimensional linear interpolation (216) is used to regenerate the contour information between edges. In addition, to further improve performance, a line is fitted to the pixel intensity data. The end values of this line are then used for the pixel intensity data. In order to still further improve performance, the pixel intensity values associated with each contour are divided into groups and each group is then encoded. Another improvement is accomplished by mean coding the residual error.

Method and apparatus for compressing and decompressing a video image

In this paper, we present a static image compression algorithm for very low bit rate applications The algorithm reduces spatial redundancy present in images by extracting and encoding edge and mean information Since the human visual system is highly sensitive to edges, an edge-based compression scheme can produce intelligible images at high compression ratios We present good quality results for facial as well as textured, 256~x~256 color images at 01 to 03 bpp The algorithm described in this paper was designed for high performance, keeping hardware implementation issues in mind In the next phase of the project, which is currently underway, this algorithm will be implemented in hardware, and new edge-based color image sequence compression algorithms will be developed to achieve compression ratios of over 100, ie, less than 012 bpp from 12 bpp Potential applications include low power, portable video telephones

Ujjaval Y. Desai

Papers

Method and apparatus for compressing and decompressing a video image

Edge and Mean based Image Compression