Multiple Description Image Coding Research Articles

Predictive side decoding for human-centered multiple description image coding

Multiple description coding (MDC) provides a favorable solution for human-centered image communication, which takes into account people’s varying watching situations as well as people’s demand for real-time image display. As an effective technique for MDC, three-description lattice vector quantization (3D-LVQ) is considered for image coding in this paper. Based on intra- and inter-correlation in the 3D-LVQ index assignment as well as wavelet intra-subband correlation, a novel predictive decoding method for 3D-LVQ-based image coding is proposed to enhance side decoding performance, which attempts to predict lost descriptions (sublattice points) in a good way for better reconstructions of wavelet vectors (fine lattice points) in the side decoding. Experimental results validate effectiveness of the proposed decoding scheme in terms of rate-distortion performance.

A New Polyphase Down-Sampling Based Multiple Description Image Coding.

Multiple description coding (MDC) is an efficient source coding technique for error-prone transmission over multiple channels. In this paper, we focus on the design of a new polyphase down-sampling based MDC (NPDS-MDC) for image signals. The encoding of our proposed NPDS-MDC consists of three steps. First, we perform down-sampling on each N×N image block according to the quincunx down-sampling pattern. Second, we propose a new transform and apply it to the down-sampled pixels to produce the side descriptions. Third, we develop an error compensation algorithm to reduce the compression distortion occurring on the down-sampled pixels. In our scheme, the side decoding is performed posterior to image interpolation with reference to the down-sampled compressed pixels. Moreover, the central decoding is achieved by interlacing the side descriptions. We also propose a compression-constrained central deblocking algorithm to further improve the efficiency of the central decoding. The experimental results indicate that our proposed MDC scheme offers clearly superior performance, especially at high bit rates, as compared to the state-of-the-art methods for various types of images.

KSVD-Based Multiple Description Image Coding

In this paper, we present a new multiple description coding scheme, which is based on a sparse dictionary training method called K singular value decomposition (KSVD). In the proposed scheme, each description encodes one source subset with a small quantization stepsize, and other subsets are predictively coded with a large quantization stepsize. The source processed by the KSVD becomes sparse, which can improve the coding efficiency. The proposed scheme is then applied to lapped transform-based multiple description image coding. Finally, image coding results show that the proposed scheme achieves a better performance than the current state-of-the-art multiple description coding methods.

Standard-Compliant Multiple Description Image Coding Based on Convolutional Neural Networks

Standard-Compliant Multiple Description Image Coding Based on Convolutional Neural Networks

Adaptive reconstruction based multiple description coding with randomly offset quantizations

In this paper, we propose a novel multiple description image coding (MDC) method,which can be called adaptive reconstruction based multiple description coding with randomly offset quantizations (ARMDROQ). In this scheme, two prediction modes are proposed, which are be defined as mode 1 and mode 2 respectively. Specifically, in the proposed ARMDROQ scheme, the input image is partitioned into M subsets and obtaining M descriptions. Then one subset of each description is encoded and decoded directly, while others are predicted encoded and decoded by using adaptive prediction modes. The experimental results show that the proposed scheme achieves better performance than other existing methods.

Image compression-based multiple description transform coding using NSCT and OMP approximation

In this paper, we present a novel multiple description transform image coding architecture, which uses an attractive transform called non-subsampled contourlet transform (NSCT). It combines NSCT and orthogonal matching pursuit algorithm (OMP) to give a sparse representation of images, aiming at solving the compression problem due to the redundancy property of NSCT. In this way, OMP turns to give a solution to remove the redundancies. We evaluate the performance of our image coder in the case of four descriptions that are dispatched over different channels. The experimentations show that the proposed method is efficient and the potential using NSCT than DWT in multiple description image coding, is evaluated by PSNR in each case of packet loss, where every description can reconstruct the image with acceptable fidelity, the later is much better if all descriptions are available.

Image compression-based multiple description transform coding using NSCT and OMP approximation

In this paper, we present a novel multiple description transform image coding architecture, which uses an attractive transform called non-subsampled contourlet transform (NSCT). It combines NSCT and orthogonal matching pursuit algorithm (OMP) to give a sparse representation of images, aiming at solving the compression problem due to the redundancy property of NSCT. In this way, OMP turns to give a solution to remove the redundancies. We evaluate the performance of our image coder in the case of four descriptions that are dispatched over different channels. The experimentations show that the proposed method is efficient and the potential using NSCT than DWT in multiple description image coding, is evaluated by PSNR in each case of packet loss, where every description can reconstruct the image with acceptable fidelity, the later is much better if all descriptions are available.

JND-based Multiple Description Image Coding

In this paper, a novel multiple description image coding (MDC) scheme is proposed, which is based on the characteristics of the human visual model. Due to the inherent characteristics of human vision, the human eye can only perceive the change of the specific thresholds, that is, the just noticeable difference (JND) thresholds. Therefore, JND model is applied to improve MDC syetem. This paper calculates the DCT coefficients firstly, and then they are compared with the JND thresholds. The data that is less than the JND thresholds can be neglected, which will improve the coding efficiency. Compared with other existing methods, the experimental results of the proposed method are superior.

Multiple Description Coding With Randomly and Uniformly Offset Quantizers

In this paper, two multiple description coding schemes are developed, based on prediction-induced randomly offset quantizers and unequal-deadzone-induced near-uniformly offset quantizers, respectively. In both schemes, each description encodes one source subset with a small quantization stepsize, and other subsets are predictively coded with a large quantization stepsize. In the first method, due to predictive coding, the quantization bins that a coefficient belongs to in different descriptions are randomly overlapped. The optimal reconstruction is obtained by finding the intersection of all received bins. In the second method, joint dequantization is also used, but near-uniform offsets are created among different low-rate quantizers by quantizing the predictions and by employing unequal deadzones. By generalizing the recently developed random quantization theory, the closed-form expression of the expected distortion is obtained for the first method, and a lower bound is obtained for the second method. The schemes are then applied to lapped transform-based multiple description image coding. The closed-form expressions enable the optimization of the lapped transform. An iterative algorithm is also developed to facilitate the optimization. Theoretical analyzes and image coding results show that both schemes achieve better performance than other methods in this category.

Multiple Description Image Coding Based on Delta-Sigma Quantization With Rate-Distortion Optimization

Recently, Østergaard and Zamir revealed the connection between multiple description coding and delta-sigma quantization (DSQ). The principle has been applied to image coding, with main focus on the framework where each block is processed separately. In this brief, we propose a two-channel multiple description image coding scheme that performs inter-block processing. The source image is first rearranged into a block sequence. Then, vector DSQ is performed with a bank of noise-shaping filters. Their coefficients as well as the quantization steps are chosen by a rate-distortion optimization algorithm. A post-processing algorithm is proposed for side decoding. Experiment results show the improvement achieved by the proposed scheme in terms of both peak signal-to-noise ratio values and subjective quality.

Total-variation based picture reconstruction in multiple description image and video coding

This paper studies how to reconstruct pictures with the best possible quality upon receiving more than one description at the decoder side of a multiple description coding (MDC) system, assuming that an MDC encoder has been fixed at the encoder side to generate multiple descriptions. To this end, we formulate the problem into a total variation (TV) regularized optimization in which all received descriptions are regarded as targets to form multiple fidelity terms. Two solutions are then developed. First, we solve a standard Lagrange-type optimization involving multiple Lagrange multipliers, and this approach is applicable to any MDC encoder. Second, when multiple quantizers with different step-sizes or dead-zones are used to generate individual descriptions, we make use of the intersection of the overlapped quantization intervals (in the transform domain) in all received descriptions. Both solutions are demonstrated to offer a quality gain (subjective as well as objective) over what can be achieved in the existing methods. In particular, the second approach is found to offer the best gain consistently when a large number of descriptions are needed.

A three-layer scheme for M-channel multiple description image coding

A three-layer scheme is developed for M-channel multiple description image coding. In each description, a subset of the source is encoded in the first layer. In the second layer, the remaining subsets are encoded sequentially by predicting from the already encoded subsets. Each description can thus produce a coarse reconstruction of the source. When multiple descriptions are received, a refined reconstruction is obtained by fusing all coarse reconstructions. A third layer is further included to refine the reconstruction when only one description is lost, which dominates when the probability of channel error is low. We first derive the closed-form expressions of the expected distortion of the system for 1-D sources. The proposed scheme is then applied to lapped transform-based image coding, where we formulate and obtain the optimal lapped transform. Image coding results show that the proposed method outperforms other latest schemes.

Efficient Multiple Description Image Coding over Error-Prone Channels Based on Frame Expansion

由量化框架扩展系数所产生的多描述图像编码，在差错信道下具有良好的图像恢复性能。该文提出一种基于框架扩展的高效多描述图像编码。首先构造一种在量化噪声下具有均方误差意义上最优框架重建的均匀紧支框架。然后以小波零树作为框架扩展的基本运算单元，在小波域对不同子带信息采用不同维度的框架进行扩展。通过不同维度的框架扩展实现对不同子带信息的不同保护。框架扩展后的数据经量化编码形成多描述码流。实验结果证明，相较于其他几种基于框架扩展的方法，该文方法在同样的信道条件下具有更好的图像差错恢复性能。

A framework for error protection of region of interest coded images and videos

In this paper, we propose a framework for unequal error protection (UEP) of image and video streaming over a wireless channel. Our framework of allocating the parity symbols associated with error control coding to the image or video codestream takes advantage of the different levels of importance that particular spatial regions of visual multimedia content has to human observers. As such, it provides stronger protection against transmission impairments to those parts of an image or video stream that correspond to the regions of interest (ROIs) while weaker protection is applied to the background (BG). For this purpose, an image or video stream represented by a sequence of packets is split into smaller cells in such a way that certain cells contain the parts of a codestream that represent ROIs and the last cell carries solely BG information. The available parity budget obtained from the given code rate is then distributed among these cells based on their contribution to the overall perceptual quality of a reconstructed image or video. A dynamic programming approach is utilized to facilitate optimal allocation of parity to ROIs and BG for ROI based UEP. The performance of the proposed ROI based UEP scheme is analyzed and compared with both the optimal UEP without ROI processing and equal error protection (EEP) in terms of an objective perceptual quality metric, the structural similarity (SSIM) index. Performance results validate the effectiveness of our framework and the superior performance of the proposed UEP scheme compared to EEP. The performance of the proposed UEP scheme matches well with that of the optimal UEP without ROI processing, especially, for multiple spatial description image and video coding while computational complexity can be kept much lower.

Two-Stage Diversity-Based Multiple Description Image Coding

In this letter, a diversity-based two-description image coding scheme is firstly presented and analyzed, for which a two-stage coding is introduced to facilitate the tuning of central/side distortion tradeoff. By subsampling the central decoded errors from the first stage to constitute the second part for each description, respectively, we show that not only the central distortion but also the side distortion can be reduced with the second stage information. We further generalize the proposed scheme to 4-description coding. Experiment results demonstrate that the proposed scheme outperforms the state-of-the-art techniques in terms of both central and side coding performance.

Directional lifting-based wavelet transform for multiple description image coding

In this paper, we address the problem of image transmission over unreliable networks, such as the Internet, where packet losses occur. The most recent literature indicates that multiple description (MD) is a promising coding approach to handle this issue. The traditional spatial domain MD image coding techniques usually result in the very low coding efficiency. This paper proposes a new architecture for MD image coding, which is based on directional wavelet transform that removes redundancy among spatial and wavelet domain. In addition, a novel interpolation method using reproducing kernel filter (RKF) reconstructs one description with full resolution. The results show that the proposed technique is far better than other existing method.

Multiple Description Image Coding Based on Lagrangian Rate Allocation

In this paper, a novel multiple description coding technique is proposed, based on optimal Lagrangian rate allocation. The method assumes the coded data consists of independently coded blocks. Initially, all the blocks are coded at two different rates. Then blocks are split into two subsets with similar rate distortion characteristics; two balanced descriptions are generated by combining code blocks belonging to the two subsets encoded at opposite rates. A theoretical analysis of the approach is carried out, and the optimal rate distortion conditions are worked out. The method is successfully applied to the JPEG 2000 standard and simulation results show a noticeable performance improvement with respect to state-of-the art algorithms. The proposed technique enables easy tuning of the required coding redundancy. Moreover, the generated streams are fully compatible with Part 1 of the standard.

Scalable Multiple-Description Image Coding Based on Embedded Quantization

Scalable Multiple-Description Image Coding Based on Embedded Quantization

Scalable Multiple-Description Image Coding Based on Embedded Quantization

Scalable multiple-description (MD) coding allows for fine-grain rate adaptation as well as robust coding of the input source. In this paper, we present a new approach for scalable MD coding of images, which couples the multiresolution nature of the wavelet transform with the robustness and scalability features provided by embedded multiple-description scalar quantization (EMDSQ). Two coding systems are proposed that rely on quadtree coding to compress the side descriptions produced by EMDSQ. The proposed systems are capable of dynamically adapting the bitrate to the available bandwidth while providing robustness to data losses. Experiments performed under different simulated network conditions demonstrate the effectiveness of the proposed scalable MD approach for image streaming over error-prone channels.

Content-based multiple description image coding

The multiple description coding method proposed in this paper provides the least amount of degradation, caused by loss of descriptors, for those areas of the image which are of greater interest. This is achieved by employing a nonlinear geometrical transform to add redundancy mainly to the area of interest followed by a partitioning of the transformed image into subimages which are coded and transmitted separately. Simulations show that this approach yields acceptable performance even when only one descriptor is received.