Progressive Image Transmission Scheme Research Articles

Progressive Image Transmission Scheme in Wireless Multimedia Sensor Networks

Progressive Image Transmission Scheme in Wireless Multimedia Sensor Networks

A Progressive Image Transmission Method Based on Discrete Wavelet Transform (DWT)

In this paper, a wavelet-based progressive image transmission (PIT) scheme is proposed. Here a combined method is proposed to reduce the image browsing time. The proposed scheme transforms a digital image from spatial domain into frequency domain by using discrete wavelet transformation. For wavelet transformation phase we have used Haar wavelet transformation. But it is computationally rigorous. Using concurrent computing we have significantly reduced computation time overhead as well as transmission time. According to the experimental results, the proposed scheme provides the accuracy of reconstructed image and the image browsing time reduces significantly.

A fast progressive image transmission scheme using block truncation coding by pattern fitting

In this paper, we have proposed a novel progressive image transmission scheme. In the present method, the concept of the BTC-PF is used for faster decoding. Here, images are decomposed into a number of blocks based on smoothness criterion. The smooth blocks are encoded by block means and the others are by BTC-PF method. To encode a block by BTC-PF method, the codebook is organized like a full search progressive transmission tree which helps greatly in efficient progressive transmission. The present method provides good image quality at low bit-rate and faster decoding compared to other spatial domain progressive transmission methods. We extend this method for color images also. In color image coding, each color plane is encoded separately and then the encoded information of the planes are transmitted in interleaving manner to obtain color images right from the early stages.

Image progressive transmission by quadtree and block truncation coding

A novel progressive image transmission scheme that is capable of achieving good reconstructed image quality at a low computational cost is proposed. The quadtree segmentation technique is employed to exploit the spatial similarity among neighbouring pixels. Besides, the mean values and the absolute moment block truncation coding with bit plane omission are used to encode smooth blocks and complex blocks respectively. By choosing suitable controlling thresholds in the quadtree segmentation technique, multiple stages of reconstructed images can be obtained at low bit rates.

AN EFFICIENT PROGRESSIVE IMAGE TRANSMISSION SCHEME FOR CHINESE CALLIGRAPHY

We invented a progressive image transmission scheme that is used for transmitting Chinese calligraphy. The scheme employs the property of simple colors of calligraphy images to design a method of transmitting images phase by phase. Overall, our scheme can achieve the following two goals. One is compressing the image data to reduce the transmission time while the other is gaining less response time by using progressive image transmission. Furthermore, the recovered image still maintains the colors of the seals with high image quality.

Low-complexity progressive image transmission scheme based on quadtree segmentation

A novel progressive image transmission scheme based on the quadtree segmentation technique is introduced in this paper. A 3-level quadtree is used in the quadtree segmentation technique to partition the original image into blocks of different sizes. Image blocks of different sizes are encoded by their block mean values. The relatively addressing technique is employed to cut down the storage cost of block mean values. In the proposed scheme, the number of image hierarchies can be adaptively selected according to the specific applications. By exploiting inter-pixel correlation and differently sized blocks for segmentation, the proposed scheme provides good image qualities at low bit rates and consumes very little computational cost in both image encoding and decoding procedures. It is quite suitable for real-time progressive image transmission.

Lossless compression-based progressive image transmission scheme

The lossless compression of images is widely used in medical imaging and remote sensing applications. Also, progressive transmission of images is often desirable because it can reduce the transmission bits of an image. Therefore, combining the features of lossless compression and progressive transmission of images has been intensely researched. The bitplane method (BPM) is the simplest way to implement a lossless progressive image transmission system. In the present paper, a new block-based scheme for lossless progressive image transmission is proposed. This scheme will reduce the transmission load and improve the image quality of BPM. This method first performs a quantization operation upon the blocks of an image. Next, these blocks are encoded with fewer bits, and the bits are then transmitted phase by phase. The experimental results show that the image quality of this method is better than those in the BPM and improved BPM in related traditional works under the same transmission load. Moreover, during the first phase, the difference in peak signal-to-noise ratio between the present method and BPM is exactly equal up to 8.85 dB. This method is therefore effective for lossless progressive image transmission.

New progressive image transmission based on quadtree and shading approach with resolution control

Employing the quadtree and Gouraud shading approach, this paper first presents a modified image compression method called the QSC. Then, based on the proposed compression method, a new progressive image transmission (PIT) scheme is presented. In the proposed PIT scheme, the encoding algorithm partitions all the quadtree nodes into several disjoint subsets such that the resolution of nodes in each subset must be in the specified interval. The decoding algorithm reconstructs the partitioned quadtree from coarse to the required quality stage by stage. Experimental results reveal that under the similar peak signal to noise ratio (PSNR) and bits per pixel (bpp), the proposed PIT scheme has a better feature-preserving capability when compared to the reduced-difference pyramid PIT scheme [Optic. Eng. 28 (7) (1989) 708] and the progressive JPEG [JPEB: Still Image Data Compression Standard, New York, 1993].

A new progressive colour image transmission scheme for the World Wide Web

In this paper, a new progressive image transmission scheme based on the RGB colour space is presented. The scheme aimed at providing an alternative progressive image transmission algorithm besides the currently popular progressive JPEG and interlaced GIF on the internet. In contrast to the traditional way of building up spatial resolution progressively, the new scheme uses a different approach by attempting to transfer colour information progressively, making the reconstruction of a sharp and clear grey scale image possible at an early stage of the transmission. Experimental results suggest that this scheme is particularly useful for the World Wide Web where very often spatial details are of primary importance.

Polynomial Approximation Coding for Progressive Image Transmission

This paper presents a progressive image transmission scheme in which polynomial approximation coding is effectively applied to encoding residual images at each stage. This polynomial approximation coding is derived from a regressive model based on the relation between a set of data and their positions. Because the close relation is embedded in a certain polynomial function, the highly efficient compression can be implemented and the decimation and interpolation are also easily realized at the receiving side. Simulations and experimental results are discussed in the later part of this paper.

Variable block size coding of images with hybrid quantization

This paper describes a progressive image transmission (PIT) scheme using a variable block size coding technique in conjunction with a variety of quantization schemes in the transform domain. The proposed scheme uses a region growing technique to partition the images so that regions of different sizes can be addressed using a small amount of side information. This segmentation divides the image into five different regions that vary in size based on the details within the image. High detail blocks are classified into four different categories based on the energy distribution followed by vector quantization (VQ), and low-detail blocks are encoded with scalar quantization (SQ). Progressive refinement is achieved by proper masking of the information in the transform domain. Simulation results show that the reconstructed images preserve fine and pleasant qualities based on both subjective and mean square error criteria. Also, the receiver reconstructs more details in each stage so that the observer can recognize the image quickly.

Pyramid-structured progressive image transmission using quantisation error delivery in transform domains

The authors propose a pyramid-structured progressive image transmission scheme in which quantisation error delivery is applied to the transform coefficients of the images represented by the pyramid. Quantisation error delivery makes it possible to reprocess at the lower level the information lost at each level of the pyramid. This is one important method for achieving lossless reproduction of an original image in pyramid-structured progressive image transmission. When the images represented by the pyramid are compressed by the transform and the quantisation is applied to the transform coefficients, however, the existing approach in the literature requires the inverse transform of the quantisation error, which is a computational overhead. To remove this drawback, the authors propose a new method to accomplish quantisation error delivery directly in the transform domain. Using the proposed method, the quantisation error at the transform coefficient of each level can be delivered and incorporated in the transform coefficient of the lower level. The simulation results show that the proposed method also has excellent coding performance for the intermediate levels of the pyramid.

Progressive Image Transmission Using LOT with Two-Channel Conjugate VQ

A progressive image transmission (PIT) scheme based on the classified two-channel conjugate VQ (TCCVQ) technique in the lapped orthogonal transform (LOT) domain is proposed. Conventional block transform coding of images using DCT produces in general undesirable block-artifacts at low bit rates. In this paper, image blocks are transformed using the LOT and classified into four classes based on their structural properties and further subdivided adaptively into subvectors depending on the LOT coefficient statistics to improve the reconstructed image quality by adaptive bit allocation. The subvectors are quantized using the two-channel conjugate VQ technique which has less computational complexity and less storage memory, and is more robust against channel errors. The vector quantized subvectors are transmitted and decoded in stages so that an image is progressively reconstructed, i.e., initially a crude version followed by quality build up in successive stages as the occasion demands in interactive visual communications. Coding tests using computer simulations show that the LOT/TCCVQ-based PIT of images is an effective coding scheme. The results are also compared with those obtained using conventional classified VQ in both the DCT and LOT domains.

Progressive image transmission by dynamic binary thresholding

A new progressive image transmission scheme in the spatial domain is developed. The proposed scheme is demonstrated to be applicable to many interactive image communications. The progressive transmission of images could solve the problems related to the lengthy time required to transmit high-resolution images over low-speed channels. The proposed scheme is required to be computationally simple but result in a bit rate improvement. This technique is also demonstrated to be capable of developing operators such as a selective fill-in function. This scheme has encoded an approximate binary image, initially using context-free encoding. The approximate binary image is represented by two lists of identical elements maintained by both sides of the communication, with each list recording the locations of regions of homogeneous intensity. The dynamic thresholding technique is then applied to progressively refine the image. The dynamic threshold is defined as the mean of intensities for each stage. The original list is partitioned by the thresholdings. The information required for updating the lists is compressed by both the prefix coding and the runlength coding. An image can be progressively refined in the future because both sides of the communication maintain the locational lists of the same content. Several computer simulations using the proposed scheme and the bit-plane method are used to illustrate the advantages of the proposed scheme, which is confirmed by performance comparisons to obtain a better result than the bit-plane method.

Human visual system weighted progressive image transmission using lapped orthogonal transform/classified vector quantization

A progressive image transmission (PIT) scheme based on the classified transform vector quantization (CVQ) technique using the lapped orthogonal transform (LOT) and human visual system (HVS) weighting is proposed. Conventional block transform coding of images using the discrete cosine transform (DCT) produces, in general, undesirable blocking artifacts at low bit rates. Here image blocks are transformed using the LOT and classified into four classes based on their structural properties and further subdivided adaptively into subvectors depending on the LOT coefficient statistics with HVS weighting to improve the reconstructed image quality by adaptive bit allocation. The subvectors are vector quantized and transmitted progressively. Coding tests using computer simulations show that the LOT/CVQ-based PIT of images is an effective coding scheme. The results are also compared with those obtained using PIT/DCTVQ. The LOT/CVQ-based PIT reduces the blocking artifact significantly.

Human visual weighted progressive image transmission

A progressive image transmission scheme which combines transform coding with the human visual system (HVS) model is developed. The adaptive transform coding of W.H. Chen and C.H. Smith (1977) is utilized to classify an image into four equally populated subblocks based on their AC energies. The modulation transfer function (MTF) of the HVS model is obtained experimentally, based on processing a number of test images. A simplified technique for incorporating the MTF into the discrete cosine transform (DCT) domain is utilized. In the hierarchical image buildup, the image is first reconstructed from the DC coefficients of all subblocks. Further transmission hierarchy of transform coefficients and consequent image buildup are dependent on their HVS weighted variances. The HVS weighted reconstructed images are compared to the ones without any weighting at several stages. The HVS weighted progressive image transmission results in perceptually higher quality images compared to the unweighted scheme. >

Progressive image transmission using vector quantization on images in pyramid form

A progressive image transmission scheme in which vector quantization is applied to images represented by pyramids is proposed. A mean pyramid representation of an image is first built up by forming a sequence of reduced-size images by averaging over blocks of 2*2 pixels. A difference pyramid is then built up by taking the differences between successive levels in the mean pyramid. Progressive transmission is achieved by sending all the nodes in the difference pyramid starting from the top level and ending at the bottom level. The kth approximate image can be formed by adding the information of level k to the previously reproduced (k-1)st approximation. To gain efficiency, vector quantization is applied to the difference pyramid of the image on a level-by-level basis. If the errors due to quantization at level k are properly delivered and included in the next level, k+1, then it is demonstrated that the original image can be reconstructed. An entropy coder is used to encode the final residual error image losslessly, thus ensuring perfect reproduction of the original image. The experiments demonstrate that it is possible to achieve simultaneously lossless and progressive transmission with compression. At the intermediate level, the use of vector quantization results in a coding gain over that obtained using only a Huffman coder. Excellent reproduction is achieved at a bit rate of only 0.06 bits/pixel. >

Lossless progressive image transmission by residual error vector quantisation

A progressive image transmission scheme is presented. This scheme has an iterative structure in which vector quantisation is iteratively applied to the residual error images of previous quantisation and an entropy coder is responsible for coding the final error image. At each stage, a fraction of the image information is transmitted and utilised in reconstructing successive approximations of the image. Iterative coding of the residual error images makes it possible to transmit the image progressively, at the same time further compression is achieved by applying vector quantisation at each stage. Lossless reproduction is guaranteed by an entropy coder at the final stage. The simulation results demonstrate that the proposed scheme achieves, simultaneously, lossless, progressive transmission of the image at an appreciable amount of data compression. Finally, iterative vector quantisation is shown to outperform single-stage vector quantisation.

Progressive image transmission by transform coefficient residual error quantization

A progressive image transmission scheme based on iterative transform coding structure is proposed for application in interactive image communication over low-bandwidth channels. The scheme not only provides progressive transmission, but also guarantees lossless reproduction combined with a degree of compression. The image to be transmitted undergoes an orthogonal transform, and the transform coefficients are quantized (scalar or vector) before transmission. The novelty is that the residual error array due to quantization is iteratively fedback and requantized (scalar or vector); the coded residual error information is progressively transmitted and utilized in reconstructing the successive approximations. It is shown that the average reconstruction error variance converges to zero as the number of iterative stages approaches infinity. In practice, lossless reproduction can be achieved with a small number of iterations by using an entropy coder on the final residual-error image. Computer simulation results demonstrate the effectiveness of the technique.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>