Embedded block coding with optimized truncation pdf

Fingerprint image has been aligned by rotating through an angle before feature vector is computed ebbcot matched. Simulation results show that this algorithm has good BER performance, low complexity and low hardware resource utilization, and it would be well applied in the future.

Our proposed fingerprint verification algorithm is based on image-based fingerprint matching. Instead of using the intensity value of the regions, we propose to use corner response function CRF as the distribution algrithm the weights of COG.

On the condition of no increasing in the decoding complexity, it makes the error-correcting performance improved by adding the appropriate scaling factor based on the min-sum algorithm MSAand it is very suitable for akgorithm implementation. This website uses cookies to improve your experience while you navigate through the website. Out of these cookies, the cookies that are categorized as necessary are stored on your browser as they are as essential for the working of basic functionalities of the website.

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This paper presents a novel sub-pixel corner detection algorithm for camera calibration. The phase of complex signals is wrapped since it can only be measured modulo-2; unwrapping searches for the 2-combinations that minimize the discontinuity of the unwrapped phase, as only the unwrapped phase can be analyzed and interpreted by further processing.

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Necessary cookies are absolutely essential for the website to function properly. This category only includes cookies that ensures basic functionalities and security features of the website.

These cookies do not store any personal information. Search for:. American Investigator. Stored in server memory is a subband decomposition of an image. The subband decomposition includes a low resolution subband and a plurality of higher resolution subbands.

Each subband is coded as a plurality of blocks, with each block representing a region of the image. Different resolutions of the regions are represented in different subband blocks. The server memory is further encoded with a program that instructs the server processor to place the low resolution subband on the network in response to a network request for the image. The server program further instructs the server processor to place at least one additional block on the network in response to a network request for a region of the image.

The additional block or blocks provide a higher resolution of the requested region. The client includes a second processor and memory for storing a program The client program instructs the client processor to send network requests for images and regions of the image. The network requests may be generated interactively by a user.

The client program further instructs the client processor to receive blocks sent by the server in response to the network requests, and reconstruct an image and region-of-interest from the blocks received on the network In the alternative, the client may include a chip and off-chip memory for reconstructing the image and regions of interest.

Running on the client is a program such a web browser. A user enters a URL of a map of the country, and the client places a request for the map on the network block The server , which stores a subband decomposition of the map, receives the network request, accesses blocks in a low subband of the decomposition, and sends all of the low subband blocks to the client block The client receives the blocks and reconstructs a low resolution image of the entire map block The low resolution map is displayed to the user.

Using an input device such as a mouse, the user clicks on a region of the map, and the client generates a request for the region of interest. The request is sent to the server block The server receives the request and accesses blocks corresponding to the region-of-interest. The blocks are taken across different subbands. The server sends these higher subband blocks to the client block The client receives the blocks and reconstructs the region-of-interest.

The region-of-interest is displayed to the user. The resolution of the reconstructed region that is, the detail of the region of interest depends upon the subbands that are accessed by the server This client-server application can involve a large compressed image that resides on a remotely located server and that is accessed and reconstructed interactively by an individual client interested in a smaller region of the image.

The server only sends those higher resolution code blocks that are relevant to the region of interest. The region of support of the subband synthesis filters should be taken into account in determining the set of subband samples which are covered by the blocks that are sent to the client. Blocks at lower resolution levels may span a substantial portion of the image so that more information is transmitted than the client has actually been requested. This is done to ensure that the region of interest is correctly reconstructed.

In most region of interest decoding applications, however, an interactive user will be able to pan across a larger region of the image over time, so that the amount of new information which is transmitted during such operations will be roughly commensurate with the size of the region which is ultimately covered, provided that previously received code blocks are properly cached and reused. Each block of each subband can be transmitted progressively so that available network bandwidth can be utilized to progressively improve the quality of a region of interest.

This will depend upon the amount of time the interactive user spends on that region before panning to a new region of interest. This is a particularly attractive feature for interactive browsing of large images over the internet where most consumers have very low bandwidth links and limited patience. Another client-server application of block coding involves selectively refining regions of a large image based on an accumulated history of client requests the server could perform this step at block of FIG.

In the event that the whole image or a large portion of the image is requested, more rate can be allocated to the blocks corresponding to those regions of the image most often requested in the past, with the hope that the interactive user is primarily interested in these regions.

The regions of interest will thus have been transmitted at a higher quality level in anticipation of the client's preference. Additionally, if storage space on the server becomes tight, and more data must be accommodated, the bitstreams corresponding to less frequently requested blocks of images can be truncated to free up space. Depending upon requirements of the client , the server might send some blocks with greater fidelity than others; in an interactive setting, the server might continually send higher fidelity updates for the current region-of-interest until the user moves to a different region of interest.

The multi-resolution property of the Wavelet transform may be exploited to eliminate these multiple copies of the same image; however, the amount of distortion which can be tolerated in any given subband depends strongly upon the resolution of the image it will be used to reconstruct. Consequently, to achieve resolution-of-interest access with a single compressed image, an embedded representation of each subband is provided. That is, the compression and decompression are performed by general purpose computers, which are programmed to compress an image into the embedded bitstream and reconstruct the image from the embedded bitstream.

The compression system includes an ASIC or some other physically distinct device and an external memory store e. There is also a destination or source for the layered bitstream or its constituent code words.

To implement the full Wavelet decomposition, the transform engine writes the LL subband samples back out to the external memory store , so that the same transform engine can later be applied to these LL subband samples to generate another level in the decomposition. Once 2K new lines have been generated for the LL band, the transform engine is applied to that band to generate K lines from each subband in the next resolution level, and so on.

The length of lines is halved for each additional level in the Wavelet decomposition tree. In this way, the engine processes 2K input lines. Every second time it does this, the transform engine is invoked again to form subbands in the next lower resolution level, whose lines are half as long, so the number of sample transactions for the next level is one quarter as large. Different scenarios for processing the subband samples will now be provided.

In one scenario, the subband samples are written back to the external memory store as they are generated. Once a sufficient number of lines of samples from a subband have been buffered, a block coding engine codes an entire row of blocks.

This allows the storage is can be recycled. However, for modest values of K, this buffering of subband code blocks in external memory makes the largest impact on external memory bandwidth. In some applications, external memory bandwidth may be a scarce resource; therefore, a second scenario is considered, in which the value of K is so large that the transform engine actually produces entire code blocks incrementally as it moves across the image from left to right.

The blocks could be buffered in a separate external memory which is fast and small e. Although the Wavelet transform is assumed to have an infinite number of resolution levels, with the typical Mallat decomposition, five or six resolution levels are used.

Image samples are assumed to arrive from the application compression or are consumed by the application decompression one line at a time, starting from the top of the image and working toward the bottom.

The original image samples are assumed to have 8-bit precision, while all subband samples and intermediate subband results e. As soon as all twelve samples are available for any given row, they are passed through a 9-sample shift register to generate four new horizontally transformed samples for each input line: two horizontal low-pass samples; and two horizontal high-pass samples.

The input region of support for generating these four output samples is actually only eleven input samples. The four output samples are buffered in four separate 9-sample shift registers , which provide an output of the transform engine The transform engine is also executed recursively on successively lower resolution levels. The illustration is intended to give a flavor of the types of functional blocks and interactions which might be involved in a real implementation.

Not all bits in the relevant sample values are examined during each coding pass. Only a few state bits for each sample are maintained. The various coding passes have access to at most one magnitude bit-plane and the sign bit-plane. The code block samples may be stored in bit-plane order, rather than sample order at the output of the transform engine Various shift registers maintain moving windows into a particular sub-block; the windows each span the width of the sub-block and advance either from the top to the bottom or vice versa, depending upon the particular coding pass being considered.

Shift registers to are used to simplify the implementation of a moving sample window which shifts from left to right or vice versa, depending upon the particular coding pass being considered. In this way, the only signals brought out of the shift registers are those which define the relevant state information in the neighborhood of a given sample. The size of the neighborhood is different for different types of information, which is reflected in the different shift register sizes.

Examples for the coding operations will now be provided. The examples for the coding operations are based on the following. The state of the shift registers is to be re-initialized on sub-block boundaries. For a sub-block containing 16 rows, a total of 18 rows of significance state bits and 18 rows of sign bits are read, whenever this information is needed during a coding pass. The state bits, sign-bits and magnitude bits are all packed into individually addressable bytes with a scan-line ordering within the block.

The visited state bits F[m,n] are reset to zero at the beginning of this pass and so need not be read in. The Backward Significance Propagation pass is virtually identical to the Forward Significance Propagation pass, except that the visited state information F[m,n] is also be read from the internal memory The distortion estimation may be implemented with the aid of a small logic circuit or two very small lookup tables.

The coding engine may also incorporate a module for computing the minimum number of bits required to decode any leading subset of the symbols. The quad tree coding engine, represented by block , has a negligible impact on complexity.

The quad tree coding engine may be trivially implemented by maintaining two state bits with each node: one to represent whether or not the node was significant in a previous bit-plane; and one to represent whether or not the node is significant in the current bit-plane. The state information can be updated as each new bit-plane sample v p [m,n] is written into the internal memory , or at a variety of other points.

Separate memory accesses are not performed by the quad-tree coding engine. Thus disclosed is an invention that compresses data into an embedded bitstream and reconstructs the data from the embedded bitstream with greater scalability, random access, error resilience, bit rate control and coding efficiency.

Scalability is better because the encoder constructs bitstreams that are optimized for decompression at a single bit-rate, at a small collection of well-defined bit rates i. The availability of independently encoded embedded bitstreams for each block provides an efficient scheme for achieving a particular target bit-rate or reconstructed image distortion bound, without having to pass through the image more than once.

Although multi-pass rate control schemes can give better compression performance for small images, they become quite impractical for large images such as those produced by modern scanners, or by satellite imagers. The ability to pass through the image once in raster-scan order, allows the compression process to proceed with only modest memory requirements and without the need to ever buffer up the entire image in the uncompressed or transformed domain.

Individual block bitstreams may be truncated before the entire image has been compressed, as a buffer size constraint is gradually approached. This is quite different from rate-control approaches which adjust a quantizer step size parameter as the buffer begins to fill up, because the decision to truncate a block's bitstream may be taken long after the block was initially coded when more information about the compressibility of other parts of the image is available.

Moreover, blocks may be truncated multiple times, making only very conservative decisions at first. The use of fractional bit-planes creates a more finely spaced set of available bit-rates and distortion values, and hence improves the performance of the post-compression rate-distortion optimization algorithm.

The encoder and decoder according to the present invention also have low complexity and low memory requirements. The distortion estimation does not add substantial complexity to the implementation of compression. Quantization is simple.

Unlike encoders that scale a quantization step parameter in order to achieve some desired bit-rate, the encoder according to the present invention may use a constant base quantization step size for all images and all bit-rates.

Moreover, any discrepancy between this base step size and an exact power of two can be absorbed into the Wavelet filter coefficients, so that the quantization operation is essentially eliminated. Independent block coding provides very strong support for error resilience. Code bytes associated with each embedded bitstream may be assigned progressively lower priority; however, in the event that some of these bytes are lost or corrupted, the effect does not propagate beyond the boundary of the block.

It is therefore possible to realize the benefits of prioritized significance as well as bounded influence to concoct a potent defense against transmission errors. The independent block coding and syntax provides the ability to selectively decompress particular regions of the image and thereby support region-of-interest decoding. By performing block coding on a subband decomposition that is, after the image has been decomposed , the invention does not produce poor compression and significant visible artifacts that have been known to occur in region-of-interest decoding in which the original image is divided into tiles and the tiles are independently compressed.

The present invention is not limited to the applications described above. For example, the invention could be applied to satellite imagery. It may be used by any application that is compatible with the emerging JPEG compression standard. The applications for SNR scalability are, of course, not limited to rate control; other important applications include progressive transmission, efficient distribution of images of heterogeneous networks, and error resilient image transmission. Moreover, the invention is not limited to the specific embodiments described and illustrated above.

The invention is not limited to any type of entropy encoding. Therefore, the invention is not limited to the specific embodiments described and illustrated above. Instead, the invention is construed according to the claims that follow. What is claimed is: 1. A method for processing a plurality of subbands of a subband decomposition, the method comprising:.

The method of claim 1 , wherein transform coefficients of the blocks are quantized prior to coding, a constant base quantization being used as a step size.

The method of claim 1 , wherein a multi-level quad tree decomposition is performed on each block to identify significant transform coefficients in the blocks. The method of claim 1 , wherein each bit-plane is coded in multiple passes, sub-blocks containing significant coefficients with respect to a previous bit-plane being coded on all but a final pass, sub-blocks containing significant coefficients with respect to a current bit-plane being coded on the final pass.

The method of claim 4 , wherein the multiple passes include a Forward Significance Propagation coding pass, a Backward Significance Propagation coding pass, a Magnitude Refinement coding pass, and a Normalization coding pass. The method of claim 4 , wherein the bit-plane coding is performed by using arithmetic coding operations including Zero Coding, Run-Length Coding, Sign Coding and Magnitude Refinement.

The method of claim 1 , further comprising generating a block bitstream for each block; gathering distortion statistics about each of the blocks as the blocks; and using the distortion statistics to identify candidate truncation points for each of the block bitstreams. The method of claim 7 , wherein a given bitstream is truncated by determining rate-distortion slopes at different truncation points of the given bitstream. The method of claim 1 , wherein context during the coding is shared among sub-blocks of a block.

The method of claim 11 , wherein the layered bitstream includes multiple layers, wherein each layer corresponds to a discrete bit rate, and wherein portions of the block bitstreams are ordered according to bit rate. The method of claim 11 , wherein the layered bitstream includes multiple layers, wherein the layers correspond to different levels of image resolution, and wherein portions of the block bitstreams are ordered according to image resolution.

The proposed algorithm is computationally very efficient and can be implemented on Real-Time Systems. Then, the location and scale of the key points is fixed by the three-dimensional quadratic function. The results show that watermarked Ebot image is nearly rate-distortion optimized, the decoder algorithmm recover the original image, the watermark can be detected effectively, and it is robust against geometric attacking such as rotating attack, cropping and scaling attack.

First of all, the scale transformation of original image is adopted by the Gaussian kernel to building the DOG multi-scale pyramid. Circuits and Systems for Video Technology. Then WSR watermarking survival rate is used to search for the optimized truncation points by running the EBCOT process, the truncation point is adjusted ebot stay at the bit plane boundary and it is used as the dividing point to split magnitude bits of wavelet coefficients, which is then sent to EBCOT for encoding.

Within these regions, the center of gravity COG method is used to gain sub-pixel corner detection. The phase of complex signals is wrapped since it can only be measured modulo-2; unwrapping searches for the 2-combinations that minimize the discontinuity of the unwrapped phase, as only the unwrapped phase can be analyzed and interpreted by further processing.