A family of stereoscopic image compression algorithms using wavelet transforms

With the standardization of JPEG-2000, wavelet-based image and video compression technologies are gradually replacing the popular DCT-based methods. In parallel to this, recent developments in autostereoscopic display technology is now threatening to revolutionize the way in which consumers are used to enjoying the traditional 2D display based electronic media such as television, computer and movies. However, due to the two-fold bandwidth/storage space requirement of stereoscopic imaging, an essential requirement of a stereo imaging system is efficient data compression. In this thesis, seven wavelet-based stereo image compression algorithms are proposed, to take advantage of the higher data compaction capability and better flexibility of wavelets. In the proposed CODEC I, block-based disparity estimation/compensation (DE/DC) is performed in pixel domain. However, this results in an inefficiency when DWT is applied on the whole predictive error image that results from the DE process. This is because of the existence of artificial block boundaries between error blocks in the predictive error image. To overcome this problem, in the remaining proposed CODECs, DE/DC is performed in the wavelet domain. Due to the multiresolution nature of the wavelet domain, two methods of disparity estimation and compensation have been proposed. The first method is performing DEJDC in each subband of the lowest/coarsest resolution level and then propagating the disparity vectors obtained to the corresponding subbands of higher/finer resolution. Note that DE is not performed in every subband due to the high overhead bits that could be required for the coding of disparity vectors of all subbands. This method is being used in CODEC II. In the second method, DEJDC is performed m the wavelet-block domain. This enables disparity estimation to be performed m all subbands simultaneously without increasing the overhead bits required for the coding disparity vectors. This method is used by CODEC III. However, performing disparity estimation/compensation in all subbands would result in a significant improvement of CODEC III. To further improve the performance of CODEC ill, pioneering wavelet-block search technique is implemented in CODEC IV. The pioneering wavelet-block search technique enables the right/predicted image to be reconstructed at the decoder end without the need of transmitting the disparity vectors. In proposed CODEC V, pioneering block search is performed in all subbands of DWT decomposition which results in an improvement of its performance. Further, the CODEC IV and V are able to perform at very low bit rates(< 0.15 bpp). In CODEC VI and CODEC VII, Overlapped Block Disparity Compensation (OBDC) is used with & without the need of coding disparity vector. Our experiment results showed that no significant coding gains could be obtained for these CODECs over CODEC IV & V. All proposed CODECs m this thesis are wavelet-based stereo image coding algorithms that maximise the flexibility and benefits offered by wavelet transform technology when applied to stereo imaging. In addition the use of a baseline-JPEG coding architecture would enable the easy adaptation of the proposed algorithms within systems originally built for DCT-based coding. This is an important feature that would be useful during an era where DCT-based technology is only slowly being phased out to give way for DWT based compression technology. In addition, this thesis proposed a stereo image coding algorithm that uses JPEG-2000 technology as the basic compression engine. The proposed CODEC, named RASTER is a rate scalable stereo image CODEC that has a unique ability to preserve the image quality at binocular depth boundaries, which is an important requirement in the design of stereo image CODEC. The experimental results have shown that the proposed CODEC is able to achieve PSNR gains of up to 3.7 dB as compared to directly transmitting the right frame using JPEG-2000

A family of stereoscopic image compression algorithms using wavelet transforms

With the standardization of JPEG-2000, wavelet-based image and video compression technologies are gradually replacing the popular DCT-based methods. In parallel to this, recent developments in autostereoscopic display technology is now threatening to revolutionize the way in which consumers are used to enjoying the traditional 2-D display based electronic media such as television, computer and movies. However, due to the two-fold bandwidth/storage space requirement of stereoscopic imaging, an essential requirement of a stereo imaging system is efficient data compression. In this thesis, seven wavelet-based stereo image compression algorithms are proposed, to take advantage of the higher data compaction capability and better flexibility of wavelets. [Continues.

Zerotree-based stereoscopic video CODEC

Due to the provision of a more natural representation of a scene in the form of left and right eye views, a stereoscopic imaging system provides a more effective method for image/video display. Unfortunately the vast amount of information that must be transmitted/stored to represent a stereo image pair/video sequence, has so far hindered its use in commercial applications. However, by properly exploiting the spatial, temporal and binocular redundancy, a stereo image pair or a sequence could be compressed and transmitted through a single monocular channel’s bandwidth without unduly sacrificing the perceived stereoscopic image quality. We propose a timely and novel framework to transmit stereoscopic data efficiently. We propose a timely and novel framework to transmit stereoscopic data efficiently. We present a new technique for coding stereo video sequences based on discrete wavelet transform DWT technology. The proposed technique particularly exploits zerotree entropy ZTE coding that makes use of the wavelet block concept to achieve low bit rate stereo video coding. One of the two image streams, namely, the main stream, is independently coded by a zerotree video CODEC, while the second stream, namely, the auxiliary stream, is predicted based on disparity compensation. A zerotree video CODEC subsequently codes the residual stream. We compare the performance of the proposed CODEC with a discrete cosine transform DCT -based, modified MPEG-2 stereo video CODEC. We show that the proposed CODEC outperforms the benchmark CODEC in coding both main and auxiliary streams

Motion compensation and very low bit rate video coding

Recently, many activities of the International Telecommunication Union (ITU) and the International Standard Organization (ISO) are leading to define new standards for very low bit-rate video coding, such as H.263 and MPEG-4 after successful applications of the international standards H.261 and MPEG-1/2 for video coding above 64kbps. However, at very low bit-rate the classic block matching based DCT video coding scheme suffers seriously from blocking artifacts which degrade the quality of reconstructed video frames considerably. To solve this problem, a new technique in which motion compensation is based on dense motion field is presented in this dissertation. Four efficient new video coding algorithms based on this new technique for very low bit-rate are proposed. (1) After studying model-based video coding algorithms, we propose an optical flow based video coding algorithm with thresh-olding techniques. A statistic model is established for distribution of intensity difference between two successive frames, and four thresholds are used to control the bit-rate and the quality of reconstructed frames. It outperforms the typical model-based techniques in terms of complexity and quality of reconstructed frames. (2) An efficient algorithm using DCT coded optical flow. It is found that dense motion fields can be modeled as the first order auto-regressive model, and efficiently compressed with DCT technique, hence achieving very low bit-rate and higher visual quality than the H.263/TMN5. (3) A region-based discrete wavelet transform video coding algorithm. This algorithm implements dense motion field and regions are segmented according to their content significance. The DWT is applied to residual images region by region, and bits are adaptively allocated to regions. It improves the visual quality and PSNR of significant regions while maintaining low bit-rate. (4) A segmentation-based video coding algorithm for stereo sequence. A correlation-feedback algorithm with Kalman filter is utilized to improve the accuracy of optical flow fields. Three criteria, which are associated with 3-D information, 2-D connectivity and motion vector fields, respectively, are defined for object segmentation. A chain code is utilized to code the shapes of the segmented objects. it can achieve very high compression ratio up to several thousands

Summative Stereoscopic Image Compression using Arithmetic Coding

Image compression targets at plummeting the amount of bits required for image representation for save storage space and speed up the transmission over network. The reduction of size helps to store more images in the disk and take less transfer time in the data network. Stereoscopic image refers to a three dimensional (3D) image that is perceived by the human brain as the transformation of two images that is being sent to the left and right human eyes with distinct phases. However, storing of these images takes twice space than a single image and hence the motivation for this novel approach called Summative Stereoscopic Image Compression using Arithmetic Coding (S2ICAC) where the difference and average of these stereo pair images are calculated, quantized in the case of lossy approach and unquantized in the case of lossless approach, and arithmetic coding is applied. The experimental result analysis indicates that the proposed method achieves high compression ratio and high PSNR value. The proposed method is also compared with JPEG 2000 Position Based Coding Scheme(JPEG 2000 PBCS) and Stereoscopic Image Compression using Huffman Coding (SICHC). From the experimental analysis, it is observed that S2ICAC outperforms JPEG 2000 PBCS as well as SICHC

Dense light field coding: a survey

Light Field (LF) imaging is a promising solution for providing more immersive and closer to reality multimedia experiences to end-users with unprecedented creative freedom and flexibility for applications in different areas, such as virtual and augmented reality. Due to the recent technological advances in optics, sensor manufacturing and available transmission bandwidth, as well as the investment of many tech giants in this area, it is expected that soon many LF transmission systems will be available to both consumers and professionals. Recognizing this, novel standardization initiatives have recently emerged in both the Joint Photographic Experts Group (JPEG) and the Moving Picture Experts Group (MPEG), triggering the discussion on the deployment of LF coding solutions to efficiently handle the massive amount of data involved in such systems. Since then, the topic of LF content coding has become a booming research area, attracting the attention of many researchers worldwide. In this context, this paper provides a comprehensive survey of the most relevant LF coding solutions proposed in the literature, focusing on angularly dense LFs. Special attention is placed on a thorough description of the different LF coding methods and on the main concepts related to this relevant area. Moreover, comprehensive insights are presented into open research challenges and future research directions for LF coding.info:eu-repo/semantics/publishedVersio

Novel entropy coding and its application of the compression of 3D image and video signals

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe broadcast industry is moving future Digital Television towards Super high resolution TV (4k or 8k) and/or 3D TV. This ultimately will increase the demand on data rate and subsequently the demand for highly efficient codecs. One of the techniques that researchers found it one of the promising technologies in the industry in the next few years is 3D Integral Image and Video due to its simplicity and mimics the reality, independently on viewer aid, one of the challenges of the 3D Integral technology is to improve the compression algorithms to adequate the high resolution and exploit the advantages of the characteristics of this technology. The research scope of this thesis includes designing a novel coding for the 3D Integral image and video compression. Firstly to address the compression of 3D Integral imaging the research proposes novel entropy coding which will be implemented first on 2D traditional images content in order to compare it with the other traditional common standards then will be applied on 3D Integra image and video. This approach seeks to achieve high performance represented by high image quality and low bit rate in association with low computational complexity. Secondly, new algorithm will be proposed in an attempt to improve and develop the transform techniques performance, initially by using a new adaptive 3D-DCT algorithm then by proposing a new hybrid 3D DWT-DCT algorithm via exploiting the advantages of each technique and get rid of the artifact that each technique of them suffers from. Finally, the proposed entropy coding will be further implemented to the 3D integral video in association with another proposed algorithm that based on calculating the motion vector on the average viewpoint for each frame. This approach seeks to minimize the complexity and reduce the speed without affecting the Human Visual System (HVS) performance. Number of block matching techniques will be used to investigate the best block matching technique that is adequate for the new proposed 3D integral video algorithm

Neural distributed image compression with cross-attention feature alignment

We consider the problem of compressing an information source when a correlated one is available as side information only at the decoder side, which is a special case of the distributed source coding problem in information theory. In particular, we consider a pair of stereo images, which have overlapping fields of view, and are captured by a synchronized and calibrated pair of cameras as correlated image sources. In previously proposed methods, the encoder transforms the input image to a latent representation using a deep neural network, and compresses the quantized latent representation losslessly using entropy coding. The decoder decodes the entropy-coded quantized latent representation, and reconstructs the input image using this representation and the available side information. In the proposed method, the decoder employs a cross-attention module to align the feature maps obtained from the received latent representation of the input image and a latent representation of the side information. We argue that aligning the correlated patches in the feature maps allows better utilization of the side information. We empirically demonstrate the competitiveness of the proposed algorithm on KITTI and Cityscape datasets of stereo image pairs. Our experimental results show that the proposed architecture is able to exploit the decoder-only side information in a more efficient manner compared to previous works