Low-complexity progressive image transmission scheme based on quadtree segmentation

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.

Segmentation of images is used for several purposes such as estimation of the boundary of an object, shape analysis, contour detection, texture segmentation, and classification of objects within an image. Despite the existence of several methods and techniques for segmenting images, this task still remains a crucial problem. In our research we have developed a neural network-based fuzzy clustering technique to segment images into regions of specific interest using a quadtree segmentation approach. Since different regions of an image contain varying amount of detail, it is advantageous to segment the regions into blocks of different sizes depending on the content of information present within each block. As the global features of an image are distributed over a wider span of the image and the finer details are concentrated in limited regions, a quadtree segmentation algorithm can efficiently tackle the problem of segmenting images of all kinds. However, block-based techniques tend to introduce blocking artifacts and this problem can be avoided by using a neuro-fuzzy clustering scheme to merge the neighboring blocks of similar regions in a smooth fashion. The proposed algorithm has been applied to images of different kinds and has yielded promising results.

The low bit rate, error resilience image coding method proposed here takes into account the content of an image and yields the least amount of degradation, caused by data loss, in those areas of the image which are of greater interest. This is achieved by employing a non-linear geometrical transform to add redundancy mainly to the region of interest (ROI). The nonlinear transform shifts most of the high frequency components of the image inside ROI to lower frequencies therefore, most of the high frequency components can be discarded. This is achieved effectively by employing the progressive mode of JPEG for encoding the nonlinear transformed image. Simulations show that this approach has acceptable performance. Moreover, the method proposed can be implemented through pre- and post-processing of the image data, without modification to the source codecs (e.g., JPEG).

Image compression and progressive transmission (PIT) is a technique used as an alternative solution to the communication problem, where there is a need to transmit huge data such as in medical image transmission. The technique divides image that is to be transmitted into several phases and effectively provides a fairly accurate reconstruction of the original image in every phase. This paper proposes a novel architecture that combines bit-plane slicing, DD-dual-tree DWT, and SPIHT. The bit-plane slicing separates the image into eight planes; each bit plane is then decomposed with wavelet. The wavelet coefficients in different sub-bands are then encoded using SPIHT and transmitted progressively and reconstructed by combining the data received in each phase with the previous phase data. Experimental results have shown that the proposed technique has better efficiency. Even at the lowest SPIHT rate of 0.1 bits/pixel for a 512 × 512 gray scale medical image, the reconstructed image shows higher quality at third phase itself, which makes it an efficient choice for transmission of medical images even at low speed communication channel.

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.

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.

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.

Hiding information in an image in a way that does not affect the original cover image pixels or cause a permanent distortion after extracting that information is known as reversible data hiding technology. Many reversible data hiding schemes have been proposed and successfully applied in medical and military applications. Such schemes are developed to ensure digital images' authenticity and integrity without any distortion on the original images. They guarantee that any attempt to change the watermarked image will be detected by the image owner. In this paper, a novel algorithm is proposed to reversibly hide data into encrypted grayscale medical images in a separable manner. The proposed algorithm hides two watermarks in a given encrypted image. The first watermark is embedded by replacing selected encrypted image pixels based on data hiding key, and the second watermark is embedded in the watermarked encrypted image using the histogram shifting reversible data hiding method. The experimental results demonstrate that the proposed algorithm has high embedding capacity, high visual image quality, and high entropy.

Zero-Watermarking in Transform Domain and Quadtree Decomposition for Under Water Images Captured by Robot

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, joint source-channel decoding (JSCD) methods are designed for JPEG2000 progressive image transmission over both AWGN and flat fading channels. A regular low-density parity-check (LDPC) code is selected as the error correcting code. Error resilience mode is used in the JPEG2000 codec. A framework for the density evolution analysis is given to show its validation for the proposed JSCD methods. A general modification algorithm that utilises feedback information from the channel decoder is proposed to update the log-likelihood ratio (LLR) of partial codeword bits. Simulation results show that the JSCD design accelerates the iterative LDPC decoding procedure while the overall performance in terms of the PSNR for reconstructed images outperforms the non-joint decoding approach.

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.

Adaptive Image Coding with Harmonic Angular Filters

The objective of this research is to develop a novel image encryption method that can be used to considerably increase the security of encrypted images. To solve this image security problem, we propose a distributed homomorphic image encryption scheme where the images of interest are those in the visible electromagnetic spectrum. In our encryption phase, a red green blue (RGB) image is first separated into its constituent channel images, and then the numerical intensity value of a pixel from each channel is written as a sum of smaller pixel intensity sub-values, leading to having several component images for each of the R, G, and B-channel images. A homomorphic encryption function is used to separately encrypted each of the pixel intensity sub-values in each component image using an encryption key, leading to a distributed image encryption approach. Each of the encrypted component images can be compressed before transmission and/or storage. In our decryption phase, each encrypted component image is decompressed if necessary, and then the homomorphic property of the encryption function is used to transform the product of individually encrypted pixel intensity sub-values in each encrypted component images, to the encryption of their sum, before applying the corresponding decryption function with a decryption key to recover the original pixel's intensity values for each channel image, and then recovering the original RGB image. Furthermore, a special case of an RGB image encryption and decryption where a pixel's intensity value from each channel is written as a sum of only two sub-values is implemented and simulated with a software. The resulting cipher-images are subject to a range of security tests and analyses. Results from these tests shown that our proposed homomorphic image encryption scheme is robust and can resist security attacks, as well as increases the security of the associated encrypted images. Our proposed homomorphic image encryption scheme has produced highly secure encrypted images.