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Frames, which in finite dimensions are spanning sets of vectors, generalize the notion of bases and provide a useful tool for modeling the measurement (or sampling) process in several modern signal processing applications. In the digital era, the measurement process is typically followed by a quantization, or digitization step that allows for storage, transmission, and processing using digital devices. One family of quantization methods, popular for its robustness to errors caused by circuit imperfections and for its ability to act on the measurements progressively, is Sigma-Delta quantization. In the finite frame setting, Sigma-Delta quantization, unlike scalar quantization, has recently been shown to exploit the redundancy in the measurement process leading to a more efficient rate distortion performance. Nevertheless, on its own, it is not known whether Sigma-Delta quantization can provide optimal rate-distortion performance. In this note, we show that a simple post-processing step consisting of a discrete, random Johnson-Lindenstrauss embedding of the resulting bit-stream yields near-optimal rate distortion performance, with high probability. In other words, it near optimally compresses the resulting bitstream. Our result holds for a wide variety of frames, including smooth frames and random frames.

Multicomponent data have become popular in several scientific fields such as forest monitoring, environmental studies, or sea water temperature detection. Nowadays, this multicomponent data can be collected more than one time per year for the same region. This generates different instances in time of multicomponent data, also called 4D-Data (1D Temporal + 1D Spectral + 2D Spatial). For multicomponent data, it is important to take into account inter-band redundancy to produce a more compact representation of the image by packing the energy into fewer number of bands, thus enabling a higher compression performance. The principal decorrelators used to compact the inter-band correlation redundancy are the Karhunen Loeve Transform (KLT) and Discrete Wavelet Transform (DWT). Because of the Temporal Dimension added, the inter-band redundancy among different multicomponent images is increased. In this paper we analyze the influence of the Temporal Dimension (TD) and the Spectral Dimension (SD) in 4D-Data in terms of coding performance for JPEG2000, because it has support to apply different decorrelation stages and transforms to the components through the different dimensions. We evaluate the influence to perform different decorrelators techniques to the different dimensions. Also we will assess the performance of the two main decorrelation techniques, KLT and DWT. Experimental results are provided, showing rate-distortion performances encoding 4D-Data using KLT and WT techniques to the different dimensions TD and SD.

The current rate distortion optimization (RDO) coding schemes usually use the Sum of Absolute Difference (SAD) or Sum of Square Difference (SSD) as the distortion metric. However, neither SAD nor SSD correlates with the human visual system (HVS) very well. To develop a perceptual distortion based video encoder, we employ the Structural Similarity (SSIM) Index as the distortion metric and propose a SSIM based Lagrangian perceptual distortion rate optimization (PDRO) method in this paper. Furthermore, to adapt the different input sequences dynamically, we present an adaptive Lagrange multiplier selection scheme based on the properties of the input sequences. By modeling the transformed residuals with Laplace distribution, the statistical SSIM and rate models are deduced to obtain the adaptive Lagrange multiplier. Extensive experiments show that the proposed scheme can achieve better perceptual distortion rate performance and provide better visual quality than the SAD/SSD based RDO coding scheme.

The impact of using different wavelet packet subband structures in JPEG2000 on the matching accuracy of a fingerprint recognition system is investigated. In particular, we relate rate-distortion performance as measured in PSNR to the matching scores as obtained by the recognition system. Employing wavelet packets instead of the dyadic wavelet transform turns out to be of advantage only in case of low bitrates (i.e. high compression rates). For such settings, the good performance of the WSQ structure is confirmed also in JPEG2000 and in particular we get better recognition accuracy using the WSQ structure as compared to the employment of a rate-distortion optimising subband selection approach

Scalable Video Coding (SVC) is an extension of H.264/AVC standard. The base layer of SVC is compatible with H.264/AVC standard, while the enhancement layers provide desired temporal, quality and/or spatial scalabilities. Bit-stream rewriting in SVC standard allows an SVC bit-stream to be converted to an H.264/AVC bit-stream without quality loss and preferably with low computational complexity. However, current rewriting is only supported in quality scalability rather than spatial scalability, which limits the application in many practical scenarios. In this paper, a hybrid bit-stream rewriting approach to support both quality and spatial scalability is proposed based on the principle of residue upsampling in transform domain. The computational complexity of the proposed approach is much lower than the conventional scheme of cascading transcoding. Extensive experimental results demonstrate that the loss of the rate-distortion (RD) performance of the proposed rewritable SVC bit-stream is acceptable compared with the conventional SVC bit-stream, however, the RD performance is better than that of simulcast. Furthermore, the RD performance of the H.264/AVC bit-stream rewritten from the rewritable SVC bit-stream is even better than that of the input SVC bit-stream. Compared with the cascading transcoding scheme, the proposed hybrid rewriting can achieve 0.8 dB Y-PSNR gains while saving 80% processing time on average.

Distributed video coding is typically treated as a channel coding problem among others. The encoder generates parity bits (or syndrome bits) for the source and transmits part of them to the decoder for a certain target quality. The decoder tries to reconstruct the source using the received parity bits along with the side information available. In this paper, we aim to estimate the critical number of parity bits to transmit. Having observed the uncertainty of the critical rate, we model it as a random variable, use its distribution to calculate the decoding failure probability and formulate the expected distortion. We allocate a certain bit budget among different bit-planes such that the expected distortion is minimized. Moreover, we introduce fast decoding at the encoder, which helps us to estimate the critical rate far more accurately. Eventually, we achieve up to 1.5 dB gain in rate-distortion performance at high bit rates.

Since the ultimate receivers of encoded video are human eyes, the characteristics of human visual system should be taken into consideration in the design of bit allocation to improve the perceptual video quality. In this paper, we incorporate the structural similarity index as a distortion metric and propose a novel rate-distortion model to characterize the relationship between rate and the structural similarity index. Based on the model, we develop an optimum bit allocation and rate control scheme for H.264 encoders. Experimental results show that up to 25% bitrate reduction over the JM reference software can be achieved. Subjective evaluation further confirms that the proposed scheme preserves more structural information and improves the perceptual quality of the encoded video.

In this paper we present a wavelet-based coding approach for semi-regular meshes, which spatially adapts the employed wavelet basis in the wavelet transformation of the mesh. The spatially-adaptive nature of the transform requires additional information to be stored in the bit-stream in order to allow the reconstruction of the transformed mesh at the decoder side. In order to limit this overhead, the mesh is first segmented into regions of approximately equal size. For each spatial region, a predictor is selected in a rate-distortion optimal manner by using a Lagrangian rate-distortion optimization technique. When compared against the classical wavelet transform employing the butterfly subdivision filter, experiments reveal that the proposed spatially-adaptive wavelet transform significantly decreases the energy of the wavelet coefficients for all subbands. Preliminary results show also that employing the proposed transform for the lowest-resolution subband systematically yields improved compression performance at low-to-medium bit-rates. For the Venus and Rabbit test models the compression improvements add up to 1.47 dB and 0.95 dB, respectively.

In this paper, we examine the rate-distortion performance in terms of perceptual quality of JPEG XR (ISO/IEC 29199-2 | ITU-T Rec. T.832)1 and earlier standardized image compression algorithms such as JPEG (ISO/IEC 10918-1 | ITU-T Rec. T.81)2 and JPEG 2000 (ISO/IEC 15444-1 | ITU-T Rec. T.800)3. Unfortunately, objective visual quality metrics (like MSE, PSNR, VQM, SSIM, etc.) do not always correlate well with the actual perceived image quality. In some specific cases, it is even possible that certain visible coding artifacts remain undetectable by these objective visual quality tests. As such, we conducted a series of subjective visual quality assessment tests to measure the visual performance of JPEG XR, JPEG 2000 and JPEG. This paper describes the design of the subjective visual quality assessment experiments, addressing the encountered difficulties and potential pitfalls. Our results indicate that for high bit-rates (i.e. more than 1 bpp) all three codecs more or less have an equal overall performance. However, as expected, at lower bit-rates JPEG performs significantly weaker for every tested image than JPEG 2000 and JPEG XR. On the other hand, both JPEG 2000 and JPEG XR appear to be very competitive at these low bit-rate ranges. Only for specific image content types (e.g. smooth gradient surfaces), JPEG XR appears to have some difficulties. Nevertheless, discarding the fact that JPEG 2000 offers more functionality features than JPEG XR, the latter performed very good for most images and almost in par with JPEG 2000. As a conclusion, the results of the subjective visual quality assessment tests show that JPEG XR successfully passed our verification experiments for low dynamic range imagery.

Down-sampling coding, which sub-samples the image and encodes the smaller sized images, is one of the solutions to raise the image quality at insufficiently high rates. In this work, we propose an Adaptive Down-Sampling (ADS) coding for H.264/AVC. The overall system distortion can be analyzed as the sum of the down-sampling distortion and the coding distortion. The down-sampling distortion is mainly the loss of the high frequency components that is highly dependent of the spatial difference. The coding distortion can be derived from the classical Rate-Distortion theory. For a given rate and a video sequence, the optimum down-sampling resolution-ratio can be derived by utilizing the optimum theory toward minimizing the system distortion based on the models of the two distortions. This optimal resolution-ratio is used in both down-sampling and up-sampling processes in ADS coding scheme. As a result, the rate-distortion performance of ADS coding is always higher than the fixed ratio coding or H.264/AVC by 2 to 4 dB at low to medium rates.

In this paper, we propose a practical approach to minimizing embedding impact in steganography based on syndrome coding and trellis-coded quantization and contrast its performance with bounds derived from appropriate rate-distortion bounds. We assume that each cover element can be assigned a positive scalar expressing the impact of making an embedding change at that element (single-letter distortion). The problem is to embed a given payload with minimal possible average embedding impact. This task, which can be viewed as a generalization of matrix embedding or writing on wet paper, has been approached using heuristic and suboptimal tools in the past. Here, we propose a fast and very versatile solution to this problem that can theoretically achieve performance arbitrarily close to the bound. It is based on syndrome coding using linear convolutional codes with the optimal binary quantizer implemented using the Viterbi algorithm run in the dual domain. The complexity and memory requirements of the embedding algorithm are linear w.r.t. the number of cover elements. For practitioners, we include detailed algorithms for finding good codes and their implementation. Finally, we report extensive experimental results for a large set of relative payloads and for different distortion profiles, including the wet paper channel.

This paper proposes a new compression algorithm based on multi-scale learned bases. We first explain the construction of a set of image bases using a bintree segmentation and the optimization procedure used to select the image basis from this set. We then present the sparse orthonormal transforms introduced by Sezer et al. 1 and propose some extensions tending to improve the convergence of the learning algorithm on the one hand and to adapt the transforms to the coding scheme used on the other hand. Comparisons in terms of rate-distortion performance are finally made with the current compression standards JPEG and JPEG2000.

In this paper, we propose a Multiple Description Coding (MDC) method for reliable transmission of compressed time consistent 3D dynamic meshes. It trades off reconstruction quality for error resilience to provide the best expected reconstruction of 3D mesh sequence at the decoder side. The method is based on partitioning the mesh frames into two sets by temporal subsampling and encoding each set independently by a 3D dynamic mesh coder. The encoded independent bitstreams or so-called descriptions are transmitted independently. The 3D dynamic mesh coder is based on predictive coding with spatial and temporal layered decomposition. In addition, the proposed method allows for different redundancy allocations by including a number of encoded spatial layers of the frames in the other set. The algorithm is evaluated with redundancy-rate-distortion curves and it is shown that, when one of the descriptions is lost, acceptable quality can be achieved with around 50% redundancy.

New data formats that include both video and the corresponding depth maps, such as multiview plus depth (MVD), enable new video applications in which intermediate video views (virtual views) can be generated using the transmitted/stored video views (reference views) and the corresponding depth maps as inputs. We propose a depth map coding method based on a new distortion measurement by deriving relationships between distortions in coded depth map and rendered view. In our experiments we use a codec based on H.264/AVC tools, where the rate-distortion (RD) optimization for depth encoding makes use of the new distortion metric. Our experimental results show the efficiency of the proposed method, with coding gains of up to 1.6 dB in interpolated frame quality as compared to encoding the depth maps using the same coding tools but applying RD optimization based on conventional distortion metrics.

In a conventional hybrid video coding scheme, the choice of encoding parameters (motion vectors, quantization parameters, etc.) is carried out by optimizing frame by frame the output distortion for a given rate budget. While it is well known that motion estimation naturally induces a chain of dependencies among pixels, this is usually not explicitly exploited in the coding process in order to improve overall coding efficiency. Specifically, when considering a group of pictures with an IPPP... structure, each pixel of the first frame can be thought of as the root of a tree whose children are the pixels of the subsequent frames predicted by it. In this work, we demonstrate the advantages of such a representation by showing that, in some situations, the best motion vector is not the one that minimizes the energy of the prediction residual, but the one that produces a better tree structure, e.g., one that can be globally more favorable from a rate-distortion perspective. In this new structure, pixel with a larger descendance are allocated extra rate to produce higher quality predictors. As a proof of concept, we verify this assertion by assigning the quantization parameter in a video sequence in such a way that pixels with a larger number of descendants are coded with a higher quality. In this way we are able to improve RD performance by nearly 1 dB. Our preliminary results suggest that a deeper understanding of the temporal dependencies can potentially lead to substantial gains in coding performance.

A novel video compression scheme that exploits the idea of second-order-residual (SOR) coding is proposed for high-bit-rate video applications in this work. We first study the limitation of today's high performance video coding standard, H.264/AVC, and show that it is not effective in the coding of small image features and variations for high-bit-rate video contents. For low to medium quality video streams, these small image features can be removed by the quantization process. However, when the quantization stepsize becomes small in high-bit-rate video, their existence degrades the rate-distortion coding performance significantly. To address this problem, we propose a coding scheme that decomposes the residual signals into two layers: the first-order-residual (FOR) and the second-order-residual (SOR). The FOR contains low frequency residuals while the SOR contains the high frequency residuals. We adopt the H.264/AVC for the FOR coding and propose two schemes, called SOR-freq and SOR-bp, for the SOR coding. It is shown by experimental results that the proposed FOR/SOR scheme outperforms H.264/AVC by a significant margin (with about 20% bit rate saving) in high-bit-rate video.

In this work, we propose the use of sparse signal representation techniques to solve the problem of closed-loop spatial image prediction. The reconstruction of signal in the block to predict is based on basis functions selected with the Matching Pursuit (MP)i terative algorithm, to best match a causal neighborhood. We evaluate this new method in terms of PSNR and bitrate in a H.264 / AVC encoder. Experimental results indicate an improvement of rate-distortion performance. In this paper, we also present results concerning the use of phase correlation to improve the reconstruction trough shifted-basis functions.

The performance bound on the encoding rate of an information source, as defined by the source entropy, pertains only to the lossless encoding of the data. In many practical situations, a certain degree of irreversible image degradation can be tolerated. This level of degradation is usually controlled by the user by adjusting a set of parameters, e.g., quantization intervals. A relevant question is: What is the minimum bit rate required to encode a source while keeping the resulting degradation below a certain level? This fundamental question is addressed by a branch of information theory known as rate-distortion theory [22]. Rate-distortion theory establishes theoretical performance bounds for lossy data compression according to a fidelity criterion. For a broad class of distortion measures and source models, the theory provides a rate-distortion function R(D) that has the following properties: - For any given level of distortion D, it is possible to find a coding scheme with rate arbitrarily close to R(D) and average distortion arbitrarily close to D. - It is impossible to find a code that achieves reproduction with distortion D (or better) at a rate below R(D).

In this paper, we propose an embedded satellite image compression method using the weighted zeroblock coding and optimal sorting. Unlike the conventional quad-tree coding methods such as Set Partition Embedded block (SPECK) and Embedded ZeroBlock Coder (EZBC), in the proposed weighted zeroblock coding (WZBC), 1) we use the unfixed scanning order to code a significant block-set with fewer bits and achieve variable-length quad-tree coding; 2) we exploit the significance degree of sub-blocks which is predicted by a novel context-based weighted strategy to optimize the scanning order of the variable-length quad-tree coding and obtain the weighted zeroblock coding; 3) the rate-distortion performance of WZBC is also optimized by using the above mentioned weight. In the binary mode, the new method does not employ the arithmetic coding and has a fairly low complexity. Experimental results show that the proposed WZBC in the binary coding mode can provide an excellent coding performance compared with SPECK and Set Partitioning In Hierarchical Trees (SPIHT) with arithmetic coding, and can even closely approach JPEG2000. When the arithmetic coding is extensively used, the proposed method can obtain more obvious gain.

In this paper, we propose an improved adaptive interpolation filter method for improving coding efficiency in H.264/ AVC. Although the conventional cost functions have showed a good performance in terms of rate and distortion, it still leaves room for improvement. To improve coding efficiency, we introduce a new cost function which considers the bit rates and distortion for coding the macroblock. The best filter is adaptively selected to minimize the proposed cost function. Experimental results show that the adaptive interpolation filter with the proposed cost function significantly improves the coding efficiency compared to ones using conventional cost function. It leads to about a 5.62% (1 reference frame) and 5.14% (5 reference frames) bit rate reduction on average compared to H.264/AVC, respectively.

With the rapid development of wireless video communication and remote monitoring, the encoder is required to be low-complexity and low-power. Distributed video coding effectively reduces the complexity of encoder, since it shifts a majority of computation to the decoder. Frame interpolation is a key component of typical DVC system. It reconstructs the WZ frame based on the intra coded key frame, namely side information generation. The fewer differences between the side information and original WZ frame corresponds to the lower translating bit rate. In this paper, we carry out a new motion compensated interpolation based on non-uniform motion trajectory to refine the side information. Multiple reference frames is applied to fulfill our proposal. Testing results show that the proposed scheme improves the PSNR by 0.3-0.5dB in comparison with hierarchical block matching algorithm, and upgrades the rate-distortion performance in a certain degree.

We present a novel analysis of encoding latency for arbitrary multiview video coding prediction structures. This analysis allows us to compare the efficiency of multiview prediction structures beyond rate-distortion performance, i.e., it makes it possible to select those structures that are more amenable to low latency encoding. As a result, we have developed a general framework (models and tools) for the characterization of latency that can be used for arbitrary dependency structures. Finally, we have focused on the JMVM prediction structure with the aim of deriving new structures with similar rate distortion performance and lower latency. Experimental results help us illustrate the importance of taking into consideration encoding latency when selecting a prediction structure.

It is known that quality fluctuation has a major negative effect on visual perception. In previous work, we introduced a constant-distortion bit allocation method [1] for H.263+ encoder. However, the method in [1] can not be adapted to the newest H.264/AVC encoder directly as the well-known chicken-egg dilemma resulted from the rate-distortion optimization (RDO) decision process. To solve this problem, we propose a new two stage constant-distortion bit allocation (CDBA) algorithm with enhanced rate control for H.264/AVC encoder. In stage-1, the algorithm performs RD optimization process with a constant quantization QP. Based on prediction residual signals from stage-1 and target distortion for smooth video quality purpose, the frame-level bit target is allocated by using a close-form approximations of ratedistortion relationship similar to [1], and a fast stage-2 encoding process is performed with enhanced basic unit rate control. Experimental results show that, compared with original rate control algorithm provided by H.264/AVC reference software JM12.1, the proposed constant-distortion frame-level bit allocation scheme reduces quality fluctuation and delivers much smoother PSNR on all testing sequences.

Stereoscopic video is a practical and important manner for 3-D video applications, and robust stereoscopic video transmission over error-prone networks has posed a technical challenge for stereoscopic video coding. In this paper, we present a rate-distortion optimization algorithm with inter-view refreshment for error-resilient stereoscopic video coding. First, inter-view refreshment is proposed to suppress error propagations besides intra refreshment. Then, we propose an end-to-end distortion model for stereoscopic video coding which concurrently considers network conditions, inter-view refreshment, and error concealment tools. Finally, based on the proposed end-to-end distortion model, a rate-distortion optimization algorithm is presented to adaptively select inter-view, inter and intra coding modes for error-resilient stereoscopic video coding. Simulation results show that the proposed scheme has a superior transmission efficiency improvement for stereoscopic video coding.

A new fast mode decision (FMD) algorithm for multi-view video coding (MVC) is presented. One of the multiple views is encoded based on traditional methods, which provides a mode decision (MD) map, while encoding of the other views is based on the analysis of the homogeneity of the depth map. This approach reduces the burden of the rate-distortion (RD) motion analysis based on the availability of a depth map, which is assumed to be provided by the acquisition process. Although there is a slight decrease of performance in rate-distortion terms, there is a significant reduction in computational cost.

In this paper a multiple description coding (MDC) that uses more than one multiple description scalar quantizer (MDSQ) is presented. The MDSQ merging constraints for joint decoding of descriptions obtained using side quantizers from different MDSQs are presented. In this way, the bit streams from the side quantizers resulting in significantly different rate-distortion properties are jointly decoded as opposed to that of two bit streams of similar rate-distortion performances obtained using a single MDSQ. Rate distortion performances of joint decoding of different description combinations obtained from multiple MDSQs outperforms those obtained from a single MDSQ. Robustness of the proposed approach evaluated under packet erasure channel is higher than that of the conventional approach. In addition to high robustness, we also demonstrate how this scheme is extended to successive side quantizer bin merging that is used to achieve robust quality scalability within an MDC framework.

This paper proposed a linear rate-distortion (RD) cost model for skip mode in H.264/MPEG-4 AVC. The proposed RD cost model is derived theoretically based on the quantization scheme of H.264, and the simulation results also verify that the proposed RD cost model can estimate the RD cost of skip mode accurately. Based on the proposed RD cost model, an early skip mode selection algorithm is provided. The proposed early skip mode selection method can terminate the mode selection process adaptively and works well for both low complexity and high complexity video. Experimental results show that the proposed early skip mode selection algorithm can save the encoding time up to 56% with negligible performance loss compared with the original reference encoder.

The recently standardized Scalable Video Coding(SVC) extension of H.264/AVC allows bitstream scalability with improved rate-distortion efficiency with respect to the classical Simulcasting approach, at the cost of an increased computational complexity of the encoding process. So one critical issue related to practical deployment of SVC is the complexity reduction, fundamental to use it in consumer applications. In this paper, we present a fully scalable fast motion estimation algorithm that enables an excellent complexity performance.

To acquire the optimal coding mode of each macroblock, the H.264/AVC encoder exhaustively calculates the ratedistortion cost for all available modes and chooses the minimum one as the best mode. Therefore, the mode decision process is very computationally demanding. To reduce the computation complexity of the rate-distortion cost, in this paper, we propose a novel rate estimation model for the mode decision in H.264/AVC. By modeling the transform coefficients with Generalized Gaussian distributions (GGD), a direct relationship between the magnitude and the information bits of the quantized transform coefficients is deduced. Based on this deduction, the weighted sum of quantized transform coefficients is proposed as an efficient bit-rate estimator of the residual blocks. Extensive experiments show that the proposed algorithm can save up to 30% of total encoding time with ignorable degradation in coding performance for both inter- and intra-mode decision.

Due to the huge amount of data and the increasing usage, compression of 4D medical data sets is essential. These datasets consist of a number of sampled volume elements varying in time and are compressed either with spatial transformation based (e.g. JPEG2000-3D) or motion estimation based schemes. This paper presents a combined approach incorporating both, temporal and spatial information at the same time to compress 4D medical datasets. It is adopting a very similar four-dimensional Multi-View-Coding (MVC) scheme which is known from video processing to 4D medical datasets and compares experimental results with H.264 compression. Rate distortion characteristics show the advantages of such a combined spatio-temporal approach.

In our previous paper, it has been demonstrated that principal component analysis (PCA) can outperform discrete wavelet transform (DWT) in spectral coding for hyperspectral image compression and a superior rate distortion performance can be provided in conjunction with 2-dimensional (2D) spatial coding using JPEG2000. The resulting compression algorithm is denoted as PCA+JPEG2000. In this paper, we further investigate how the data size (i.e., spatial and spectral size) influences the performance of PCA+JPEG2000 and provide a rule of thumb for PCA+JPEG2000 to perform appropriately. We will also show that using a subset of principal components (PCs) (the resulting algorithm is denoted as SubPCA+JPEG2000) can always yield a better rate distortion performance than PCA+JPEG2000 with all the PCs being preserved for compression.

Spread spectrum (SS) modulation is utilized in many watermarking applications because it offers exceptional robustness against several attacks. The embedding rate-distortion performance of SS embedding however, is relatively weak compared to quantization index modulation (QIM). This limits the relative embedding rate of SS watermarks. In this paper, we illustrate that both the embedding effciency, i.e. bits embedded per unit distortion and robustness against additive white gaussian noise (AWGN) can be improved by pre-coding of message followed by constellation adjustment on the SS detector to minimize the distortion on the cover image introduced by coded data. Our pre-coding method encodes p bits as a 2p x 1 binary vector with a single nonzero entry whose index indicates the value of the embedded bits. Our analysis show that the method improves embedding rate by approximately p/4 without increasing embedding distortion or sacrificing robustness to AWGN attacks. Experimental evaluation of the method using a set theoretic embedding framework for the watermark insertion validates our analysis.© (2008) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

For rate-distortion optimized rate allocation in JVT Scalable Video Coding (SVC), the distortion impact of every FGS NAL unit on the global reconstruction quality is calculated by repeatedly bitstream decoding, which leads to high complexity. In this paper, a fast rate allocation algorithm by modeling distortion estimation is proposed. Based on the hypothesis that DCT residual coefficients follow Laplacian distribution, we establish the distortion estimation model by calculating quantization error of each FGS NAL unit and analyzing the prediction in hierarchical B coding structure. Besides, the parameter in the model is updated according to the distribution of residual coefficients decoded at the base layer within every frame. Experimental results show that compared to the existing method of R-D optimized rate allocation in SVC, the proposed method results in a reduction in decoding time of nearly 50%, and save the runtime of rate allocation by 45.3%, while the PSNR loss of decoded sequence is only 0.04 dB on average.

In this paper, a redundant picture formation algorithm that takes into account a given redundancy rate constraint is presented for error resilient wireless video transmission without reliance on retransmissions. The algorithm assigns priorities to MBs according to two suggested metrics and ranks macroblocks accordingly. The first metric is based on an end-to-end distortion model and aims at maximising the reduction in distortion per redundancy bit. The end-to-end distortion accounts for the effects of error propagation, mismatch between the primary and redundancy description and error concealment. Macroblocks providing large distortion reduction for fewer bits spent are assigned a higher priority. The second metric employs the variance of the motion vectors of a macroblock and those of its neighbouring blocks. Results show that the rate distortion metric outperforms other examined metrics by up to 2dB. Moreover, gains over existing error resilience schemes, such as LA-RDO, are presented.

In the past few years, practical distributed video coding systems have been proposed based on Slepian-Wolf and Wyner-Ziv theorems. The quality of side information plays a critical role in the overall performance for such a system. In this paper, we present a novel approach to generating the side information using optimal filtering techniques. The motion vectors (MVs) that define the motion activity between the main information and the side information are first predicted by an optimal filter, then the MVs obtained from a decoded WZ frame by a conventional motion search method corrects the prediction results. The side information is generated from the updated MVs via a motion compensated interpolation (MCI) process and can be subsequently fed into the decoding process to further improve the quality of a decoded WZ frame. We studied several variations of optimal filters and compared them with other DVC systems in terms of rate-distortion performance.

Sign language users are eager for the freedom and convenience of video communication over cellular devices. Compression of sign language video in this setting offers unique challenges. The low bitrates available make encoding decisions extremely important, while the power constraints of the device limit the encoder complexity. The ultimate goal is to maximize the intelligibility of the conversation given the rate-constrained cellular channel and power constrained encoding device. This paper uses an objective measure of intelligibility, based on subjective testing with members of the Deaf community, for rate-distortion optimization of sign language video within the H.264 framework. Performance bounds are established by using the intelligibility metric in a Lagrangian cost function along with a trellis search to make optimal mode and quantizer decisions for each macroblock. The optimal QP values are analyzed and the unique structure of sign language is exploited in order to reduce complexity by three orders of magnitude relative to the trellis search technique with no loss in rate-distortion performance. Further reductions in complexity are made by eliminating rarely occuring modes in the encoding process. The low complexity SL optimization technique increases the measured intelligibility up to 3.5 dB, at fixed rates, and reduces rate by as much as 60% at fixed levels of intelligibility with respect to a rate control algorithm designed for aesthetic distortion as measured by MSE.

Codecs such as H.264/AVC involve computationally intensive tasks that often prohibit the real-time implementation. It has been observed that the complexity of such video encoders can be tuned gracefully to a desired level through the use of a smaller set of macroblock types in mode decision and a lower motion vector precision in motion estimation. The rate-distortion performance, however, will be affected consequently. In this paper, we propose a flexible syntax mechanism (FSM) to tune the encoder complexity while maintaining a sufficient rate-distortion performance. The key idea inherit in the proposed FSM consists of two folds: first is the specification at the higher level of the bitstream syntax both the subset of macroblock types and the precision of motion vectors to be evaluated by the encoder, and second is the redesign of the entropy coders accordingly to effectively represent the selected macroblock types and the motion vectors. Since the entropy coding is optimized in terms of the bitrate consumption specifically for the subset of macroblock modes and the motion vector precision, the rate-distortion performance will be enhanced compared to the scenario where identical entropy codes are adopted regardless. Another advantage of our approach is the intrinsic scalability in complexity for the application of video encoding under different complexity constraints. The proposed approach may be considered for the next generation of video codecs with flexible complexity profiles.

Source classification has been widely studied in conventional coding of image and video signals. This paper explores the idea of exploiting so-called classification gain in Wyner-Ziv (WZ) video coding. We first provide theoretical analysis of how source classification can lead to improved Rate-Distortion tradeoff in WZ coding and quantify the classification gain by the ratio of weighted arithmetic mean to weighted geometric mean over subsources. Then we present a practical WZ video coding algorithm based on the source classification principle. The statistics of both spatial and temporal correlation are taken into account in our classification strategy. Specifically, the subsource with the steepest R-D slope is identified to be the class of significant wavelet coefficients of the blocks that are poorly motion-compensated in WZ frames. In such classification-based approach, rate control is performed at the decoder which can be viewed as the dual to conventional video coding where R-D optimization stays with the encoder. By combining powerful LDPC codes (for generating coded information) with advanced temporal interpolation (for generating side information), we have observed that the new Wyner-Ziv coder achieves highly encouraging performance for the test sequences used in our experiments. For example, the gap between H264 JM11.0 (I-B-I-B...) and the proposed WZ video coder is dramatically reduced for foreman and hall QCIF sequences when compared with the best reported results in the literature.

Recently, several methods that divide the original bitstream of an image/video progressive wavelet based source coders into multiple correlated substreams have been proposed. The principle behind transmitting independent multiple substream is to generate multiple descriptions of the source such that the graceful degradation is achieved when transmitted over severe fading channels and lossy packet networks since some of the streams may be recovered. Noting that multiple substream can benefit from multiple independent channel paths, we naturally consider Multi-Input Multi-Output communication systems where we obtain multiple independent fading channels. Depending on several factors including: the number of antennas employed, the transmission energy, the Doppler shift (due to the motion between the transmitter antenna and receiver antennas), the total transmission rate and the distortion-rate (D-R) of the source-there exists an optimal number of balanced substreams and an optimal joint source-channel coding policy such that the expected distortion at the receiver is minimized. In this paper we derive an expected distortion function at the receiver based on all of these parameters and provide a fast real-time numerical technique to find the optimal or near optimal number of balanced substreams to be transmitted. This expected distortion is based on our derivation of the probabilistic loss patterns of a balanced multiple substream progressive source coder. The accuracy of the derived expected distortion estimator is confirmed by Monte-Carlo simulation employing Dent's modification of Jakes' model. By accurately estimating the optimal number of balanced substreams to be transmitted, a substantial gain in visual quality at low and intermediate signal-to-noise ratio (SNR) is obtained over severely fading channels. At high SNR, the single stream source coder's source efficiency makes it slightly better than the multiple substream source coder. Overall, using our analytic development, we provide a systematic real-time non-adhoc method that achieves high quality image and video at low and moderate signal-to-noise ratios in severe fading channels for single and multiple antenna systems.

In recent years digital imaging devices become an integral part of our daily lives due to the advancements in imaging, storage and wireless communication technologies. Power-Rate-Distortion efficiency is the key factor common to all resource constrained portable devices. In addition, especially in real-time wireless multimedia applications, channel adaptive and error resilient source coding techniques should be considered in conjunction with the P-R-D efficiency, since most of the time Automatic Repeat-reQuest (ARQ) and Forward Error Correction (FEC) are either not feasible or costly in terms of bandwidth efficiency delay. In this work, we focus on the scenarios of real-time video communication for resource constrained devices over bandwidth limited and lossy channels, and propose an analytic Power-channel Error-Rate-Distortion (P-E-R-D) model. In particular, probabilities of macroblocks coding modes are intelligently controlled through an optimization process according to their distinct rate-distortion-complexity performance for a given channel error rate. The framework provides theoretical guidelines for the joint analysis of error resilient source coding and resource allocation. Experimental results show that our optimal framework provides consistent rate-distortion performance gain under different power constraints.

A novel image compression scheme is presented that the directional sections are separated and transformed differently from the rest of image. The discrete directions of anisotropic pixels are calculated and then grouped to compact directional sections. One dimensional (1-D) adaptive directional lifting is continuously applied along orientations of direction sections other than applying 1-D wavelet transform alternately in two dimensions for the whole image. For the rest sections, 2-D adaptive lifting filters are applied according to pixels' positions. Our single embedded coding stream can be truncated exactly for any bit rate. Experiments have showed that large coefficients can be significantly reduced along directional sections by our transform which makes energy more compact than traditional wavelet transform. Though rate-distortion (R-D) optimization isn't exploited, the PSNR is still comparable to that of JPEG-2000 with 9/7 filters at high bit rates. And at low bit rates, the visual quality is better than that of JPEG-2000 for along directional sections both blurring and ringing artifacts can be avoided and edge preservation is good.

H.264/AVC video coding standard aims to enable to significantly improve compression performance compared to all existing video coding standards. In order to achieve that, a robust rate-distortion optimization (RDO) technique is employ to select the best coding mode. As a result, the complexity of computation increase drastically, which makes it difficult for practical applications such as real time video compression. This paper presents a fast mode selection for intra-prediction based on Pan Algorithm to reduce mode decision complexity. Pan's fast algorithm is effectively to reduce the complexity, however, such method exists some drawbacks. In this paper, Pan's fast algorithm based on edge direction histogram is improved; meanwhile, joining the relation of chroma predictive mode and luminance predictive mode, blocks. Experiment result shows that the algorithm increases the speed of intra-prediction with negligible loss of peak signal to noise ratio (PSNR).

This paper provides a detailed rate-distortion performance comparison between JPEG2000, Microsoft HD Photo, and H.264/AVC High Profile 4:4:4 I-frame coding for high-resolution still images and high-definition (HD) 1080p video sequences. This work is an extension to our previous comparative study published in previous SPIE conferences [1, 2]. Here we further optimize all three codecs for compression performance. Coding simulations are performed on a set of large-format color images captured from mainstream digital cameras and 1080p HD video sequences commonly used for H.264/AVC standardization work. Overall, our experimental results show that all three codecs offer very similar coding performances at the high-quality, high-resolution setting. Differences tend to be data-dependent: JPEG2000 with the wavelet technology tends to be the best performer with smooth spatial data; H.264/AVC High-Profile with advanced spatial prediction modes tends to cope best with more complex visual content; Microsoft HD Photo tends to be the most consistent across the board. For the still-image data sets, JPEG2000 offers the best R-D performance gains (around 0.2 to 1 dB in peak signal-to-noise ratio) over H.264/AVC High-Profile intra coding and Microsoft HD Photo. For the 1080p video data set, all three codecs offer very similar coding performance. As in [1, 2], neither do we consider scalability nor complexity in this study (JPEG2000 is operating in non-scalable, but optimal performance mode).

In this paper, we propose a general framework and practical coding methods for constructing steganographic schemes that minimize the statistical impact of embedding. By associating a cost of an embedding change with every element of the cover, we first derive bounds on the minimum theoretically achievable embedding impact and then propose a framework to achieve it in practice. The method is based on syndrome codes with low-density generator matrices (LDGM). The problem of optimally encoding a message (e.g., with the smallest embedding impact) requires a binary quantizer that performs near the rate-distortion bound. We implement this quantizer using LDGM codes with a survey propagation message-passing algorithm. Since LDGM codes are guaranteed to achieve the rate-distortion bound, the proposed methods are guaranteed to achieve the minimal embedding impact (maximal embedding efficiency). We provide detailed technical description of the method for practitioners and demonstrate its performance on matrix embedding.

Luminance and chrominance correction (LCC) is important in multi-view video coding (MVC) because it provides better rate-distortion performance when encoding video sequences captured by ill-calibrated multi-view cameras. This paper presents a robust and fast LCC algorithm based on motion compensated linear regression which reuses the motion information from the encoder. We adopt the linear weighted prediction model in H.264/AVC as our LCC model. In our experiments, the proposed LCC algorithm outperforms basic histogram matching method up to 0.4dB with only few computational overhead and zero external memory bandwidth. So, the dataflow of this method is suitable for low bandwidth/low power VLSI design for future multi-view applications.

The new video coding standard H.264/AVC employs the rate-distortion optimization (RDO) method for choosing the best coding mode. However, since it increases the encoder complexity tremendously, it is not suitable for real-time applications, such as IPTV broadcasting services. Therefore we need a fast mode decision algorithm to reduce its encoding time. In this paper, we propose a fast mode decision algorithm considering quantization parameter (QP) because we have noticed that the frequency of best modes depends on QP. In order to consider these characteristics, we use the coded block pattern (CBP) that has "0" value when all quantized discrete cosine transform (DCT) coefficients are zero. We also use both the early SKIP mode and early 16x16 mode decisions. Experimental results show that the proposed algorithm reduces the encoding time by 74.6% for the baseline profile and 72.8% for the main profile, compared to the H.264/AVC reference software.

H.264/MPEG4-AVC is the latest video codec provided by the Joint Video Team, gathering ITU-T and ISO/IEC experts. Technically there are no drastic changes compared to its predecessors H.263 and MPEG-4 part 2. It however significantly reduces the bitrate and seems to be progressively adopted by the market. The gain mainly results from the addition of efficient motion compensation tools, variable block sizes, multiple reference frames, 1/4-pel motion accuracy and powerful Skip and Direct modes. A close study of the bits repartition in the bitstream reveals that motion information can represent up to 40% of the total bitstream. As a consequence reduction of motion cost is a priority for future enhancements. This paper proposes a competition-based scheme for the prediction of the motion. It impacts the selection of the motion vectors, based on a modified rate-distortion criterion, for the Inter modes and for the Skip mode. Combined spatial and temporal predictors take benefit of temporal redundancies, where the spatial median usually fails. An average 7% bitrate saving compared to a standard H.264/MPEG4-AVC codec is reported. In addition, on the fly adaptation of the set of predictors is proposed and preliminary results are provided.

In this paper, we propose a new algorithm for wavelet-based multi-resolution motion estimation (MRME) using temporal aliasing detection (TAD). In wavelet transformed image/video signals, temporal aliasing will be severe as the motion of object increases, causing the performance of the conventional MRME algorithms to drop. To overcome this problem, we perform the temporal aliasing detection and MRME simultaneously instead of using a temporal anti-aliasing filter which changes the original signal. We show that this technique gives competitive or better performance in terms of rate-distortion (RD) for slow-varying or simple-moving video signals compared to conventional MRME employing increased search area (SA).

In this paper, we propose a statistical learning based approach to analyze the rate-distortion characteristics of multiscale binary shape coding. We employ the polynomial kernel function and incorporate rate-distortion related features for our support vector regression. e-Insensitive loss function is chosen to improve the estimation robustness. The parameter tuning is also studied. Moreover, we discuss the feature selection which helps to improve the estimation accuracy. Comparing to the traditional method, our proposed framework provides better rate distortion estimation not only on simple shapes but also on complex shapes.

In this paper, we investigate the efficacy of the weighted prediction feature provided within H.264/AVC video coding standard for error resilient video streaming. Leaky prediction has been proposed for scalable and non-scalable video coding to effectively combat transport errors. However, all prior results are based on non standard coding methods and no results have been available on the effectiveness of leaky prediction that is supported by a video coding standard. The weighted prediction feature in H.264/AVC was originally designed for improving the coding efficiency especially in presence of fading in video sequences. This paper presents a performance analysis of H.264/AVC weighted prediction feature that balances the trade-off between coding efficiency and error resilience. A theoretical analysis of rate-distortion performance of leaky prediction is provided and closed-form rate-distortion functions are derived for the error free and error drift scenarios. The theoretical results conform well to the operational results with respect to different choices of the leaky factor.