Method of isomorphic singular manifold projection still/video imagery compression

Abstract

Methods and apparatuses for still image compression, video compression and automatic target recognition are disclosed. The method of still image compression uses isomorphic singular manifold projection whereby surfaces of objects having singular manifold representations are represented by best match canonical polynomials to arrive at a model representation. The model representation is compared with the original representation to arrive at a difference. If the difference exceeds a predetermined threshold, the difference data are saved and compressed using standard lossy compression. The coefficients from the best match polynomial together with the difference data, if any, are then compressed using lossless compression. The method of motion estimation for enhanced video compression sends I frames on an "as-needed" basis, based on comparing the error between segments of a current frame and a predicted frame. If the error exceeds a predetermined threshold, which can be based on program content, the next frame sent will be an I frame. The method of automatic target recognition (ATR) including tracking, zooming, and image enhancement, uses isomorphic singular manifold projection to separate texture and sculpture portions of an image. Soft ATR is then used on the sculptured portion and hard ATR is used on the texture portion.

Description

[0001] The present invention relates to image compression systems, and in particular relates to an image compression system which provides hypercompression.[0002] BACKGROUND OF THE INVENTION[0003] Image compression reduces the amount of data necessary to represent a digital image by eliminating spatial and / or temporal redundancies in the image information. Compression is necessary in order to efficiently store and transmit still and video image information. Without compression, most applications in which image information is stored and / or transmitted would be rendered impractical or impossible.[0004] Generally speaking, there are two types of compression: lossless and lossy. Lossless compression reduces the amount of image data stored and transmitted without any information loss, i.e., without any loss in the quality of the image. Lossy compression reduces the amount of image data stored and transmitted with at least some information loss, i.e., with at least some loss of quality of...

AI Technical Summary

Problems solved by technology

Without compression, most applications in which image information is stored and / or transmitted would be rendered impractical or impossible.

Lossy compression reduces the amount of image data stored and transmitted with at least some information loss, i.e., with at least some loss of quality of the image.

In other words, external constraints for a given system may define a limited storage space available for storing the image information, or a limited bandwidth (data rate) available for transmitting the image information.

Lossy compression sacrifices image quality in order to fit the image information within the constraints of the given available storage or transmission capacity.

These techniques typically use cosine-type transforms like DCT and wavelet compression, which are specific types of transforms, and have a tendency to lose high frequency information due to limited bandwidth.

Loss of edge information is undesirable because resolution is lost as well as high frequency information.

Fractal compression, though better than most, suffers from high transmission bandwidth requirements and slow coding algorithms.

It operates at integer multiples of 64 kbps and its segmentation and model based methodology splits an image into several regions of specific shapes, and then the contour and texture parameters representing the region boundaries and approximating the region pixels, respectively, are encoded A basic difficulty with the segmentation and model-based approach is low image quality connected with the estimation of parameters in 3-D space in order to impart naturalness to the 3-D image.

The introduction of I frames asynchronously into the video bitstream in the encoder is wasteful and introduces artifacts because there is no correlation between the I frames and the B and P frames of the video.

This procedure results in wasted bandwidth.

Particularly, if an I frame has been inserted into B and P frames containing no motion, bandwidth is wasted because the I frame was essentially unnecessary yet, unfortunately, uses up significant bandwidth because of its full content.

On the other hand, if no I frame is inserted where there is a lot of motion in the video bitstream, such overwhelming and significant errors and artifacts are created that bandwidth is exceeded.

Since the bandwidth is exceeded by the creation of these errors, they will drop off and thereby create the much unwanted blocking effect in the video image.

This, however, happens only a portion of the time in standard compression techniques like MPEG.

The situation is even more stringent for continuity of communication when degradation of power budget or multi-path errors of wireless media further reduce the allowable data rate to far below 128 kbps.

Consequently, state of the art technology is far from providing multi-media parallel channelization and continuity data rates at equal to or lower than 128 kbps.

Unfortunately, due to the above limitations, state of the art compression techniques are not able to transmit high quality video in real-time on a band-limited communication channel.

In addition to transmission or storage of compressed still or moving images, another area where the state of the art is unsatisfactory is in automatic target recognition (ATR).

These methods are lacking in that Fourier analysis eliminates desired "soft edge" or contour information which is critical to human cognition.

If, on the other hand, the error is low, the compression ratio can be increased, thereby decreasing bandwidth of the signal to be stored.

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