Low bit rate, error resilient image communication using nonlinear pre and post-processing and progressive image transmission

Application of nonlinear pre- and post-processing in low bit rate, error resilient image communication

Medical images are of high volume and contain valuable diagnostic information, which have to be preserved against any archival or transmission error. Progressive transmission of data is favourable due to the fact that the region of interest (RoI) within the image can be coded without any visible error and that it speeds up the transmission of desired information due to the hierarchical region relation & transmission. In addition, this modality of communication has been modified to be interactive so the physicians can select the RoI to prioritize transmission of various regions. Wireless transmission of medical images involves construction of an effective source-channel coding to not only preserve the diagnostic information but also to enable progressively streaming the data from host to receiver [1]. A number of large medical images have been compressed and progressively transmitted with low bitrate. The blocking effect, as can be seen in JPEG coded images with the same compression ratio, has been highly reduced and the quality of the decoded images enhanced. A MATLAB-based TCP/IP connection has been established to demonstrate the proposed interactive progressive transmission system.

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.

Low-complexity progressive image transmission scheme based on quadtree segmentation

The embedded multi-resolution coding in JPEG2000 make the ROI(Region Of Interesting) coding very successful. But it is processed in the wavelet domain, resulting in having to decompose some mature JPEG2000 baseline system coder and decoder to implement the ROI function. So a low computational complexity upscaling method in space domain, deduced from the relation of the original image and the image after wavelet transform, is proposed in this paper without modifying the baseline system. By the method, we should right shift the data outside the region of interesting, then use the normal JPEG2000 system to encode and decode the image, and subsequently we can get the image by left shift that data in the space domain after decoding. The method can get high performance quality in ROI in very low bit rate. And it has low computational complexity, needn't deduce the ROI mask in the wavelet domain. As Maxshift method in the wavelet domain, the Maxshift in space domain can be deduced and it decodes the compressed image without ROI mask. That is the ROI mask transmission is not need. The cost of the simpleness is that it will produce distinct tiring edge around the ROI. The method can implement the ROI function through simple extends with the JPEG2000 baseline hardware without the ROI function and it is significative in practice.

The increasing popularity of video consumption from mobile devices requires an effective video coding strategy. To overcome diverse communication networks, video services often need to maintain sustainable quality when the available bandwidth is limited. One of the strategy for a visually-optimised video adaptation is by implementing a region-of-interest (ROI) based scalability, whereby important regions can be encoded at a higher quality while maintaining sufficient quality for the rest of the frame. The result is an improved perceived quality at the same bit rate as normal encoding, which is particularly obvious at the range of lower bit rate. However, because of the difficulties of predicting region-of-interest (ROI) accurately, there is a limited research and development of ROI-based video coding for general videos. In this paper, the phase spectrum quaternion of Fourier Transform (PQFT) method is adopted to determine the ROI. To improve the results of ROI detection, the saliency map from the PQFT is augmented with maps created from high level knowledge of factors that are known to attract human attention. Hence, maps that locate faces and emphasise the centre of the screen are used in combination with the saliency map to determine the ROI. The contribution of this paper lies on the automatic ROI detection technique for coding a low bit rate videos which include the ROI prioritisation technique to give different level of encoding qualities for multiple ROIs, and the evaluation of the proposed automatic ROI detection that is shown to have a close performance to human ROI, based on the eye fixation data.

For solving the problems of storage and transmission existing in the high-resolution photogrammetric image, an efficient photogrammetric image encoding method based on Region of Interest (ROI) is proposed according to the application characters of photogrammetric image. The ROIs of the photogrammetric image are automatically detected and extracted using the binarization segmentation and the image dilation algorithm. The description of ROI mask is achieved by combining the coefficient similarity in the same level wavelet sub-bands with specific wavelet filter at an extremely low bit rate. All ROI coefficients are compressed using the lossless method to completely reserve the image information necessary for higher measurement precision. According to the energy concentration features of wavelet transform, only the BG (Back Ground) coefficients belonging to the lowest frequency sub-band are encoded in order to improve total compression ratio. Experimental results show that the ROI mask and the main context information could be described at less than 0.03bit/pixel. Furthermore, the proposed method has improved its compression ratio by more than 6%, and reduced the encoding time above 30% as compared with those of the MaxShift method based on JPGE2000. Thus, the proposed method realizes a high efficiency encoding of photogrammetric image and lays a good foundation for the storage and transmission of the high-resolution photogrammetric image.

In this paper, we show two basic results to pyramid coding for a progressive image transmission. We first show that in a hierarchical pyramid structure, the transform coding can provide a gain over a simple scalar quantizer. The optimum bit allocation strategy among the Laplacian pyramid for transform coding is also described. Secondly, based on th.e use of transform coding, we show that especially at low bit rates a hierarchical pyramid structure provides a significant gain over encoding of an input image directly. This is particularly attractive to the progressive transmission using a hierarchical pyramid structure.

One interesting feature of the new JPEG2000 image coding standard is support of region of interest (ROI) coding using the maximum shift (Maxshift) method, which allows for arbitrarily shaped ROI image compression without shape coding or explicitly transmitting any shape information to the decoder. The major disadvantage of the Maxshift method is that it cannot adjust the scaling value which determines the degree of relative importance between the ROI and the background wavelet coefficients. The bitplane-by-bitplane shift (BbBShift) method was introduced to support both arbitrary ROI shape and arbitrary scaling without shape coding. We propose a generalized BbBShift (GBbBShift) method, which delivers much more flexibility than both Maxshift and BbBShift for degree-of-interest adjustment of the ROI with insignificant effect on coding efficiency and computational complexity. Experiments show that it can provide significantly better visual quality than Maxshift at low bit rates. GBbBShift is not compliant with the current JPEG2000 definitions. In order to use it, a new ROI coding mode would need to be added to the standard.

Iconic representation for progressive transmission of medical images

In the absence of a priori knowledge about regions of interest, a rational system for progressive transmission chooses at any truncation time among alternative spatial orientation trees for further transmission, in such a way as to avoid certain forms of behavioral inconsistency. Some rational transmission systems might exhibit aversion to risk involving gambles on the tree-dependent quality of encoding, while others favor taking such risks, but risk-prone systems as well as those with strong risk aversion appear incapable of attaining the quality of reconstructions that can be achieved with moderately risk-averse behavior. Even though the problem of estimating the parameter that controls the risk attitude in a rational transmission system is a key issue, it is not clear how to pick the risk aversion parameter for different image content. We estimate the risk aversion parameter by integrating rationality and cooperative action with the aim of preventing disproportionately large benefit gains and strong oscillations in gain at very low bit rates. Experimental results show the comparative performance of the corresponding transmission system with both the state of the art in progressive transmission set partitioning in hierarchical trees (SPIHT) and the state of art coder JPEG2000.

In image compression and image processing applications, region of interest (ROI) of images are hoped to get better compression results compares with region of back ground. Combining the advantages of general scaling based method (SBM) for the processing region of interest in JPEG2000, a novel ROI image coding method using multiwavelet-based set partitioning in hierarchical (SPIHT) algorithm is proposed. By using this coding method, the compressed bit stream are all embedded and suited for progressive transmission. Experimental results show that at the same compression ratio, although the whole image's PSNR performance of the proposed algorithm is inferior to the original multiwavelet-based SPIHT algorithm, ROI can get better reconstructed quality, especially for low bit rate compression condition.

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.

Summary form only given. We present a simple and efficient scheme for protecting a region of interest (ROI) in an image sent across generalized multiple description networks. To increase the probability that the ROI is received with high quality, we extend the unequal loss protection framework of MD-SPIHT by adding more redundancy to the ROI than to other parts of the image. The SPIHT algorithm orders data progressively and sends the globally important information first. By adding more redundancy to the earlier parts of the bit stream than to the later parts, MD-SPIHT protects data that are globally important to the image quality more than less important data. In this work, wavelet coefficients corresponding to the ROI are scaled by a large factor so that locally important information is sent in the earlier parts of the bit stream. As a result, the ROI is coded to a higher bit rate than the rest of the image and MD-SPIHT automatically assigns more redundancy to the localized ROI than to the background. Therefore, the ROI is heavily protected at the expense of lower protection in the background. The ROI has a higher probability of being received intact and its quality will be higher than the quality of the background. We show two cases in which 3 out of 8 descriptions are received for 35% redundancy assignment. Without ROI scaling, redundancy is spread to all parts of the image and the quality of the ROI is low. When the ROI is scaled, redundancy is concentrated in the ROI and the quality of the received ROI is higher.

A multiple description (MD) coding scheme encodes an image such that each description alone yields an acceptable reconstruction quality, and the descriptions together render increasingly better approximations of the image. Traditionally, each description contributes equally to the reconstruction quality throughout the image. This is inefficient from a coding perspective since all descriptions largely represent similar source information. We suggest a novel region of interest (ROI) based MD coding scheme with two descriptions and two ROIs, such that each ROI is coded into a separate description. The impact this scheme has on visual reconstruction quality and coding efficiency is investigated. We argue that ROI based MD coding is beneficial at low bit rates since it (i) introduces little redundancy between the descriptions with practically no loss in quality at the central decoder, and (ii) still produces useful side descriptions. Extensions to video coding are discussed in the concluding section.