In this work we study the 4D sparsity of light fields using as main tool the 4D- Discrete Cosine Transform. We analyze the two JPEG Pleno light field datasets, namely the lenslet-based and the High-Density Camera Array (HDCA) datasets. The results suggest that the lenslets datasets exhibit a high 4D redundancy, with a larger inter-view sparsity than the intra-view one. For the HDCA datasets, there is also 4D redundancy worthy to be exploited, yet in a smaller degree. Unlike the lenslets case, the intra-view redundancy is much larger than the inter-view one. The results and conclusions of this first study for light field imaging may have a strong impact on the current and future design of efficient codecs for this type of emerging data, notably in the context of the JPEG Pleno standard.

Light fields aim to represent visual information in 3D space. They are 4D structures that contain the images of a given scene from a sampled 2D range of viewpoints. When acquired using a lenslet camera, in addition to the ordinary intra-view redundancy, these views have a great deal of inter-view redundancy. In this work we propose a light field codec that fully exploits the 4D redundancy of light fields by using a 4D transform and hexadeca-trees. It initially divides the light field into 4D blocks and computes a 4D Discrete Cosine Transform of each one. Then the transform coefficients of the 4D block are grouped using hexadeca-trees on a bitplane-by-bitplane basis, and the generated stream is encoded using an adaptive arithmetic coder. The proposed codec has been employed to encode the JPEG Pleno lenslet light fields. The rate-distortion results have been assessed using test conditions comparable to the ones presented at the ICIP 2017 Light Field Coding Grand Challenge. The proposed codec, despite being conceptually simple, achieves competitive rate-distortion performance.

The representation of data in terms of its statistical properties is valuable in many applications. This work uses statistics obtained from 4D scene geometry to characterize, in terms of redundancy, the content produced by lenslet-based light field cameras and by high-density arrays of cameras for the JPEG Pleno Call for Proposals on Light Field Coding. This paper proposes a novel so-called geometric space-view redundancy (GSVR) descriptor, which is able to characterize the amount of redundancy in light fields thus bringing information about the trade-offs involved in effectively exploring redundancy for efficient coding. The redundancy is here measured by the probability, for each block size and range of views, that the image of a given 3D point belongs to the block in all views. For a given probability, the GSVR descriptor models the spaceview correlation, i.e. the correlation between the intra-view block dimensions and the number of views. Therefore, it is a descriptor that has application on dataset selection and encoder control and optimization. The JPEG Pleno datasets are analyzed in terms of the GSVR descriptor in all views.

In this paper, we propose a new 3D pattern matching based video compression algorithm. Spatiotemporal prediction tools are used to exploit both the temporal and the spatial redundancies, and the resulting residue is encoded using a 3D data coding extension of the Multidimensional Multiscale Parser (MMP) algorithm. MMP was originally proposed as a generic lossy data compression algorithm. A high degree of adaptivity and versatility allowed it to be competitive with state-of-the-art transform-based compression methods for a wide range of applications and data sets. The performance of the proposed algorithm is compared with that of H.264/AVC, achieving close results for some data sets, which indicates the potential of the pattern matching paradigm as an alternative to the traditional hybrid video codecs.

In this study, the authors investigate the use of multi‐scale recurrent pattern matching paradigm for lossless image compression. The multi‐scale multidimensional parser (MMP) algorithm is a successful implementation of this paradigm for lossy image compression, and can naturally perform lossless compression since it was first derived from a Lempel–Ziv lossless scheme. However, neither its recently adopted coding tools had been adapted for lossless coding nor a thorough analysis of its performance had been carried out. In this work, the authors evaluate MMP's lossless compression capability, proposing modifications for some of its predictions modes, as well as the inclusion of an adaptive prediction mode based on least squares. The residual information is also coded with well‐known techniques used in lossless compression. Experimental results for MMP show that the algorithm achieves a good performance for images such as computed generated graphics and scanned documents, whereas keeping a competitive performance for natural images. Since the algorithm's structure is exactly the same for lossless and lossy compression, the obtained results suggest that MMP is able to achieve a high compression performance for a wide range of images and rates, from lossy to lossless, without any prior analysis of the image to be coded.

In this paper, we propose a pattern-matching-based algorithm for video compression. This algorithm, named multidimensional multiscale parser (MMP)-Video, is based on the H.264/AVC video encoder, but uses a pattern-matching paradigm instead of the state-of-the-art transform-quantization-entropy encoding approach. The proposed method adopts the use of multiscale recurrent patterns to compress both spatial and temporal prediction residues, totally replacing the use of transforms and quantization. Experimental results show that the coding performance of MMP-Video is better than the one of H.264/AVC high profile, especially for medium to high bit-rates. The gains range up to 0.7 dB, showing that, in spite of its larger computational complexity, the use of multiscale recurrent pattern matching paradigm deserves being investigated as an alternative for video compression.

In this paper we present a new algorithm for chroma prediction in YUV images, based on inter component correlation. Despite the YUV color space transformation for inter component decorrelation, some dependency still exists between the Y, U and V chroma components. This dependency has been previously used to predict the chrominance data from the reconstructed luminance. In this paper we show that a chrominance component can be more efficiently predicted by using the reconstructed data from both the luminance and the remaining chrominance signal. The proposed chroma prediction is implemented and tested using the Multidimensional Multiscale Parser (MMP) image encoding algorithm. It is shown that the new color prediction mode outperforms the originally proposed prediction methods. Furthermore, by using the new color prediction scheme, MMP is consistently better than the state-of-the-art H.264/AVC for coding both for the luminance and the chrominance image components.

In this article we propose to compress depth maps using a coding scheme based on multiscale recurrent pattern matching and evaluate its impact on depth image based rendering (DIBR). Depth maps are usually converted into gray scale images and compressed like a conventional luminance signal. However, using traditional transform-based encoders to compress depth maps may result in undesired artifacts at sharp edges due to the quantization of high frequency coefficients. The Multidimensional Multiscale Parser (MMP) is a pattern matching-based encoder, that is able to preserve and efficiently encode high frequency patterns, such as edge information. This ability is critical for encoding depth map images. Experimental results for encoding depth maps show that MMP is much more efficient in a rate-distortion sense than standard image compression techniques such as JPEG2000 or H.264/AVC. In addition, the depth maps compressed with MMP generate reconstructed views with a higher quality than all other tested compression algorithms.

In this paper, we propose a new encoder for scanned compound documents, based upon a recently introduced coding paradigm called multidimensional multiscale parser (MMP). MMP uses approximate pattern matching, with adaptive multiscale dictionaries that contain concatenations of scaled versions of previously encoded image blocks. These features give MMP the ability to adjust to the input image's characteristics, resulting in high coding efficiencies for a wide range of image types. This versatility makes MMP a good candidate for compound digital document encoding. The proposed algorithm first classifies the image blocks as smooth (texture) and nonsmooth (text and graphics). Smooth and nonsmooth blocks are then compressed using different MMP-based encoders, adapted for encoding either type of blocks. The adaptive use of these two types of encoders resulted in performance gains over the original MMP algorithm, further increasing the performance advantage over the current state-of-the-art image encoders for scanned compound images, without compromising the performance for other image types.

The Multidimensional Multiscale Parser-based (MMP) image coding algorithm, when combined with flexible partitioning and predictive coding techniques (MMP-FP), provides state-of-the-art performance. In this paper we investigate the use of adaptive least-squares prediction in MMP. The linear prediction coefficients implicitly embed the local texture characteristics, and are computed based on a block's causal neighborhood (composed of already reconstructed data). Thus, the intra prediction mode is adaptively adjusted according to the local context and no extra overhead is needed for signaling the coefficients. We add this new context-adaptive linear prediction mode to the other MMP prediction modes, that are based on the ones used in H.264/AVC; the best mode is chosen through rate-distortion optimization. Simulation results show that least-squares prediction is able to significantly increase MMP-FPs rate-distortion performance for smooth images, leading to better results than the ones of state-of-theart, transform-based methods. Yet with the addition of least-squares prediction MMP-FP presents no performance loss when used for encoding non-smooth images, such as text and graphics.

In this paper we present a new segmentation method for the multidimensional multiscale parser (MMP) algorithm. In previous works we have shown that, for text and compound images, MMP has better compression efficiency than state-of-the-art transform-based encoders like JPEG2000 and H.264/AVC; however, it is still inferior to them for smooth images. In this paper we improve the performance of MMP for smooth images by employing a more flexible block segmentation scheme than the one defined in the original algorithm. The new partition scheme allows MMP to exploit the image's structure in a much more adaptive and effective way. Experimental tests have shown consistent performance gains, mainly for smooth images. When employing the new block segmentation scheme, MMP outperforms the state-of-the-art JPEG2000 and H.264/AVC Intra-frame image coding algorithms for both smooth and non-smooth images, at low to medium compression ratios.

In this work we use the multidimensional multiscale parser (MMP) algorithm to compress three-dimensional data from weather radar. MMP is based on approximate multiscale pattern matching. MMP encodes segments of an input signal using expanded and contracted versions of patterns stored in a dictionary. The dictionary is updated using concatenated and displaced versions of previously encoded segments, therefore MMP builds its own dictionary while the input data is being encoded. MMP can be adapted to compress signals of any number of dimensions, and has been successfully applied to compress two-dimensional image data and one-dimensional ECG and EMG signals. We show simulation results where our 3D version of MMP outperforms some of the best encoders in the literature.

In this work, we further develop the multidimensional multiscale parser (MMP) algorithm, a recently proposed universal lossy compression method which has been successfully applied to images as well as other types of data, as video and ECG signals. The MMP is based on approximate multiscale pattern matching, encoding blocks of an input signal using expanded and contracted versions of patterns stored in a dictionary. The dictionary is updated using expanded and contracted versions of concatenations of previously encoded blocks. This implies that MMP builds its own dictionary while the input data is being encoded, using segments of the input itself, which lends it a universal flavor. It presents a flexible structure, which allows for easily adding data-specific extensions to the base algorithm. Often, the signals to be encoded belong to a narrow class, as the one of smooth images. In these cases, one expects that some improvement can be achieved by introducing some knowledge about the source to be encoded. In this paper, we use the assumption about the smoothness of the source in order to create good context models for the probability of blocks in the dictionary. Such probability models are estimated by considering smoothness constraints around causal block boundaries. In addition, we refine the obtained probability models by also exploiting the existing knowledge about the original scale of the included blocks during the dictionary updating process. Simulation results have shown that these developments allow significant improvements over the original MMP for smooth images, while keeping its state-of-the-art performance for more complex, less smooth ones, thus improving MMP's universal character.

In this paper, we exploit a recently introduced coding algorithm called multidimensional multiscale parser (MMP) as an alternative to the traditional transform quantization-based methods. MMP uses approximate pattern matching with adaptive multiscale dictionaries that contain concatenations of scaled versions of previously encoded image blocks. We propose the use of predictive coding schemes that modify the source's probability distribution, in order to favour the efficiency of MMP's dictionary adaptation. Statistical conditioning is also used, allowing for an increased coding efficiency of the dictionaries' symbols. New dictionary design methods, that allow for an effective compromise between the introduction of new dictionary elements and the reduction of codebook redundancy, are also proposed. Experimental results validate the proposed techniques by showing consistent improvements in PSNR performance over the original MMP algorithm. When compared with state-of-the-art methods, like JPEG2000 and H.264/AVC, the proposed algorithm achieves relevant gains (up to 6 dB) for nonsmooth images and very competitive results for smooth images. These results strongly suggest that the new paradigm posed by MMP can be regarded as an alternative to the one traditionally used in image coding, for a wide range of image types.

In this paper we describe the ongoing work on a new paradigm for compressing the motion predicted error in a video coder, referred to as MMP-Video. This new coding algorithm uses the multidimensional multiscale parser image coding algorithm to encode the residue error, in a H.264/AVC based video coder. MMP has shown to perform very well as a universal still image coding method, particularly when it is combined with intra prediction schemes. In addition, previously published preliminary results have also presented MMP as a promising video coding method. In this paper, we propose new dictionary updating techniques for MMP-video. Along with other functional optimizations, these techniques allow for a significant improvement in the encoder performance. Thus, we were able to achieve considerable gains over H.264/AVC for B slices, specially for medium and high bit-rates, while maintaining equivalent performance for the P slices.

Recently, the multidimensional multiscale parser (MMP), an algorithm based on multiscale recurrent patterns, has been used to successfully compress data from ECG signals. Their quasi-periodic nature makes them natural candidates for the use of recurrent patterns. However, as many diagnostic relevant signals are far from periodic, the characteristics of MMP are not fully exploited. We have dealt with this problem by interpolating the ECG signal so that the interval between successive heart beats becomes constant. Following that, the algorithm subtracts the average period from the interpolated signal. Simulation results show that these modifications increase the rate-distortion performance.

MMP-Intra was recently proposed as a recurrent patterns based image encoder that combines the multidimensional multiscale parser (MMP) algorithm with intra prediction techniques. Our results show that this method is able to achieve considerable gains over state-of-the-art transform-based image encoders for a wide variety of types of images, like text, composed (text and graphics) and texture images, while having performance close to the one of traditional algorithms for smooth images. Because of this universal character, MMP-Intra can be regarded as a viable alternative to transform-based image coding. MMP-Intra uses a multiscale adaptive dictionary to approximate the original data blocks. It is composed of dilations, contractions and concatenations of previously encoded patterns. In this work we present a new method for controlling the dictionary adaptability, in which the dictionary is only updated if a certain distortion criterion is met in the block being encoded. Experimental results show that this scheme is able to consistently outperform the original method, while achieving relevant reductions in its computational complexity

In this paper we present a new method for image coding that is able to achieve good results over a wide range of image types. This work is based on the multidimensional multiscale parser (MMP) algorithm (M. de Carvalho et al., 2002), allied with an intra frame image predictive coding scheme. MMP has been shown to have, for a large class of image data, including texts, graphics, mixed images and textures, a compression efficiency comparable (and, in several cases, well above) to the one of state-of-the-art encoders. However, for smooth grayscale images, its performance lags behind the one of wavelet-based encoders, as JPEG2000. In this paper we propose a novel encoder using MMP with intra predictive coding, similar to the one used in the H.264/AVC video coding standard. Experimental results show that this method closes the performance gap to JPEG-2000 for smooth images, with PSNR gains of up to 1.5 dB. Yet, it maintains the excellent performance level of the MMP for other types of image data, as text, graphics and compound images, lending it a useful universal character.

For the compression of stereo images, several techniques employing the concept of disparity compensation have been developed. In this paper, we present a new method for stereo-image compression which is based on a different paradigm. It relies on approximate matching of multiscale recurrent patterns, which is an extension of ordinary pattern matching, where an image block is represented using dilated, contracted, displaced, and deformed versions of blocks already processed. To achieve this, the input image is segmented into variable-sized blocks. Each segment is represented by an element in a dictionary. The segmentation is performed according to a rate /spl times/ distortion criterion, and the dictionary is updated with the concatenation, contraction, expansion, displacement, and deformation of previously coded elements. Unlike most other methods, in this approach the disparity map is not transmitted. That is, the generation of the error images, as well as their coding and transmission, is not necessary. In brief, the proposed method presents high-quality results at the decoder end, compared to other coding methods for stereo images that transmit the disparity map.

The recently proposed method for image compression based on multi-scale recurrent patterns, the MMP (multidimensional multiscale parser) has been shown to perform well for a large class of images, specially for those containing text or graphics. However, its performance for coding smooth, gray scale images is still distant from the state of the art. In this paper we propose an extension for it, the SM-MMP (side-match MMP). In this method, as in MMP, a multidimensional signal is recursively segmented into variable-length blocks, and each segment is encoded using expansions and contractions of vectors in a dictionary. The dictionary is updated while the data is being encoded, using concatenations of expanded and contracted versions of previously encoded blocks. However, unlike MMP, in SM-MMP the dictionaries are built considering smoothness constraints around block boundaries, similar to the side-match vector quantization methods. This allows it to perform better than the MMP when the images are smooth, without sacrificing its performance for images containing text or graphics. Indeed, our simulation results show that the proposed method is effective, yielding improvements of the order of 1.5 dB over the original MMP for grayscale images, while preserving the high performance of the original MMP for graphics, text and mixed images.

A stereo image coding method using multiscale recurrent patterns is proposed. The method relies on the matching of recurrent patterns. The input image is segmented in variable-sized blocks. Each segment is coded by making use of contractions, expansions and displacements of elements of a dictionary. The segmentation is ruled by a rate distortion criterion and the dictionary is updated with the concatenation of previously coded elements. The novelty of this work is the absence of the disparity map in the stereo image coding process. That is, the burden of its calculation, the pre-and post-processing phases, the generation of the error images, as well as their coding and transmission are not necessary. In brief, the proposed method cuts off the load of the whole disparity estimation process yet presenting high-quality results at the decoder end.

We present a new rate-distortion optimized algorithm for adaptive vector quantization. It is based on approximate matching of recurrent patterns. In our approach, the input vector is segmented in variable-sized blocks. The blocks are encoded using a set of codebooks, one for each block size. The codebooks are updated while the data is encoded, with no need for any side information. Also, no prior training is required. We use dynamic programming. techniques to optimize the segmentation tree. It performs well for a wide class of sources, with very good results for highly nonstationary sources, like compound documents.

A method that reduces the blocking effect of the lossy compression of multi-dimensional data with a version of the UMMP (universal multi-scale matching pursuit) algorithm is presented. The method consists of a generalization of the previously presented UMMP which extends its use to bases with overlapping functions. We circumvent the difficult optimization problem posed by overlapping functions at the encoder by the use of two different bases, one for the encoding and another, with overlapping functions, for the decoding of the data.

A universal algorithm to compress multi-dimensional data is presented. Besides being able to explore multidimensional correlations of the data, it incorporates two other fundamental innovations when compared to its predecessors, the Lossy Lempel-Ziv (LLZ) and Hierarchical String Matching algorithms: first, instead of building a dictionary of strings to match the data, it builds a dictionary of basis functions in which the data will be decomposed in the spirit of Mallat's matching pursuits; second, any basis function in the dictionary can be dilated or contracted when used to match the data. Simulation results show that it has good coding performance for a large class of image data. With Gaussian sources it has shown good performance, outperforming LLZ algorithms for low data rates, being close to the R(D). With real image data, when LLZ fails at all rates, it has performed even better, showing a great improvement over LLZ.