Pyramid transform coding

A new vector quantization technique, called storage- and entropy-constrained multi-stage vector quantization (SECMSVQ) technique, is presented for progressive image transmission (PIT) applications. In the SECMSVQ technique, the rate constraint at each stage of the vector quantizer (VQ), and the storage size of the whole VQ are specified independently before the design. A dynamic programming technique is then used to allocate the storage size to each stage of the VQ optimally so that the quantization distortion of the VQ is minimized. The SECMSVQ is well-suited to constructing a PIT system where the transmission time and storage size are the major concerns. Moreover, simulation results show that the SECMSVQ technique can be used effectively to implement signal compression systems under the storage and rate constraints.

Current progressive image transmission (PIT) systems can not control both bit rate and resolution constraints in images. Due to the partially localised nature of the wavelet transform, the value of any pixel in the image depends on only a small number of wavelet coefficients. Thus, it is possible to specify an arbitrary region of the image and prevent that region from being badly degraded during the compression process. This can be done by representing coefficients corresponding to those regions in the wavelet space more accurately. This is accomplished by multiplying those values by an arbitrary factor before applying the embedded zerotree wavelets (EZW) coding technique. The EZW algorithm is then effectively applied to, wavelet coefficients to refine and encode the most significant ones in the wavelet space. The information of the image is thus transmitted in several successive stages. It is shown that in a PIT system not only any region of interest (ROI) can be sent and received progressively, but also bitrate and resolution constraints can be controlled simultaneously. This option becomes very effective in interactive communication channels where the available bandwidth is at a premium.

Reversible data-hiding for progressive image transmission

Low-complexity progressive image transmission scheme based on quadtree segmentation

Pyramid data structures have played an important role in progressive image transmission. Over the years, the reduced difference pyramid (RDP) has been found to be one of the best data structure. The RDP takes the differences between the neighboring nodes at the same level. The new modified difference pyramid (MDP) data structure, developed in this paper, takes the differences between successive levels. Simulation results show that both the bit rate and the complexity of the receiver for the MDP structure are lower than those for the RDP structure at the same quality. An MDP coding process which incorporates a prefilter is also proposed in this paper. Simulation results show it can provide good quality (in a subjective sense) reconstructed images at a lower bit rate than the unfiltered scheme can. Also, acceptable intermediate images are interpolated via the repetition method and via the cubic convolution method to get images which are the same size as the original. Results are compared.

The Laplacian pyramid is a versatile data structure that represents an image as a sequence of spatially filtered and decimated versions of the original image. To obtain an expanded approximation from the processed decimated samples, linear interpolation is commonly used due to its computational simplicity; however, linear interpolation does not consider the gray-level change in the neighborhood of each interpolated pixel, and thus creates unpleasant staircase effects near edges and visually annoying blocking effects in shade regions. This paper proposes a new pyramidal image coding algorithm using contour-based interpolative vector quantization (VQ) to obtain a perceptually better interpolated image through the use of edge information at each level of the pyramid. The basic idea is to code the image using VQ on a level-by-level basis in the hierarchical structure. The decimation implies that the redundancy existing over large image areas can be efficiently exploited by VQ of a low dimension. In order to improve the quality of the reconstructed image, an adaptive corrector stage is added at the final stage of the pyramid to selectively encode the residual errors as needed. The locations for correction are hierarchically predicted using the previously encoded data at the higher levels. The above scheme is a very efficient combination of VQ and contour-based interpolation in the hierarchical pyramid structure, and is amenable to progressive image transmission. Experimental results show that good reconstructed images are obtained at 0.25 bits per pixel with a reasonable coding complexity.

In this paper a new developed algorithm for compression of still images based on 2D-SOFM NN's in correspondence with the method of Inverse Difference Pyramid (IDP) decomposition is represented. The new developed algorithm is well suited to be used in Progressive Image Transmission (PIT). Advantage of the method relies on the learning process and adaptation capability of NN's to reduce the matrices computation complexity and the total number of pyramid levels required for PIT. In addition to, for image reconstruction no interpolation is needed anymore, which improves the quality of the reconstructed image.

Few networks offer sufficient bandwidth for the transmission of high resolution two- and three-dimensional medical image sets without incurring significant latency. Traditional compression methods achieve bit-rate reduction based on pixel statistics and ignore visual cues that are important in identifying visually informative regions. This paper describes an approach to managing image transmission in which spatial regions are selected and prioritized for transmission so that visually informative data is received in a timely manner. This context-based image transmission (CBIT) scheme is a lossless form of progressive image transmission (PIT) in which gross structure, represented by an approximate iconic image, is transmitted first. Each part of this iconic image is progressively updated, using a simple set of rules that take into account viewing requirements. CBIT is realized using knowledge about image composition to segment, label, prioritize, and fit geometric models to regions of an image. Tests, using neurological images, show that, with CBIT, a valuable transmitted image is received with a latency that is about one-tenth that of traditional PIT schemes. Frequently, the necessary regions of the image are transmitted in about half the time taken to transmit the full image.

In this paper, we first propose a new embedded multilevel block truncation coding (BTC) technique. Unlike Differential pulse code modulation (DPCM), Vector quantization (VQ), and general multilevel BTC algorithms which determine the image quality at a time, the embedded multilevel BTC improves the image quality largely and progressively until obtaining an image with excellent quality. In order to reduce the bit rate efficiently, we propose a perception model and utilize it to develop a pruning algorithm. The pruning algorithm removes the useless information, which the human eyes are not sensitive, generated by the embedded multilevel BTC. The simulation results indicate that the bit rate with the proposed method is much less than that with the DPCM and general multilevel BTC under the same objective criterion, PSNR, or subjective criterion. This paper also shows that the computation complexity of the proposed method is much less than that with VQ under the same high quality reconstructed image.

An efficient coding scheme for progressive transmission of gray-scale images is presented. In the scheme, a rough sketch of an image is transmitted first and the details are added later. At the final stage of transmission, a receiver can reproduce the exact replica of an original image with high coding efficiency. This kind of presentation is especially desirable for the interactive visual search of large image databases, where a viewer can recognize image content as quickly as possible, halt transmission of unwanted detail, and retry another image. This scheme utilizes subsampling and level-plane coding in a recursive way. Thus, the coding algorithm is simple and can be easily implemented even on a personal computer. Computer simulations show that the scheme achieves the mean code length of 5.24 b/pel for an image, which is almost the same as that of efficient nonprogressive coding schemes. >

In this paper, a hybrid coding scheme for progressive image transmission (PIT) based on two components, that is the smooth and edge components, is presented. The image is first decimated by using the pyramid coding technique with a decimation factor of 4:1. A multistage predictive locally adaptive vector quantization (MSPVQ) progressively transmits the decimated image that represents the smooth component of the scene. The first stage of the proposed (VQ) gives a general grasp of the Lena image at a compression ratio as high as 80:1. An adaptive VQ (LAVQ) encodes the difference image, which contains the quantization and edge error of the smooth component. By adding the encoded image components, an excellent reproduction is achieved at a low bit rate of only 0.2 bit per pixel (bpp), i.e. compression ratio of 40:1, with peak signal-to-noise ratio (SNR) of 36.92 dB. It is also shown that, for the Lena image and for the same PSNR of about 34 dB, the bit rate using the proposed algorithm is very low; only 0.2 bpp. This bit rate is lower than about 0.6 bpp using the JPEG algorithm, and much lower than that using BTC-DCT or SB-DCT algorithms. Thus the proposed algorithm results in a saving of about 0.4 in the bit rate for the same visual quality.

Fault-tolerant and progressive transmission of images

Several fragile watermarking schemes presented in the literature are either vulnerable to vector quantization (VQ) counterfeiting attacks or sacrifice localization accuracy to improve security. Using a hierarchical structure, we propose a method that thwarts the VQ attack while sustaining the superior localization properties of blockwise independent watermarking methods. In particular, we propose dividing the image into blocks in a multilevel hierarchy and calculating block signatures in this hierarchy. While signatures of small blocks on the lowest level of the hierarchy ensure superior accuracy of tamper localization, higher level block signatures provide increasing resistance to VQ attacks. At the top level, a signature calculated using the whole image completely thwarts the counterfeiting attack. Moreover, "sliding window" searches through the hierarchy enable the verification of untampered regions after an image has been cropped. We provide experimental results to demonstrate the effectiveness of our method.

With the growth of Internet, Image compression has become a popular issue. Since the traditional Vector Quantization (VQ) produces compressed images with a quality at about 27 to 30 dB or so, the techniques of quality improvement is limited. Thus in this paper, we proposed an adaptive image compression method based on VQ, which can adjust the encoding of the difference map between the original image and its restored VQ compressed version. Experimental results show that although our scheme needs to provide extra data, it can substantially improve the quality of VQ compressed images, and further be adjusted depending on the difference map from the lossy compression to lossless compression.

A robust associative watermarking technique based on vector quantization