DEEC

In this paper we propose a new compound image segmentation algorithm, specially developed for a digital document encoding scheme based on the Multidimensional Multiscale Parser (MMP) encoding algorithm. In order to increase the MMP algorithm performance, a block based segmentation defines a mask that divides the input image into two types of blocks: text-like blocks and non-text, or smooth image-like blocks. Different versions of the MMP algorithm are then applied on each image layer. The segmentation algorithm presented in this paper is based in two steps. The first step relies on morphological filtering and image gradient analysis. In the second step, false text blocks are eliminated using a morphological based algorithm. Experimental results show that this algorithm is not only efficient for our encoder, but also improves the segmentation accuracy in relation to other methods proposed in the literature.

In this paper, we propose a pattern-matchingbased 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-quantizationentropy 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. Index Terms-Dictionary-based coding, recurrent pattern matching, vector quantization, video coding. I. Introduction S EVERAL decades of video coding standards have confirmed the hybrid model as the most successful architecture for video compression algorithms. In these methods, a motion-compensated prediction or an intraframe prediction stage reduces the temporal and spatial redundancy of the signal. The resultant residue is then compressed using traditional transform-quantization-entropy coding methods, which efficiently exploit the remaining data's statistical correlation. This general architecture was also maintained in the successful H.264/AVC [1] standard, which achieves more than Manuscript

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.

A low bit rate video coding technique that uses spatio-temporal geometric transforms is presented. Motion compensation based on the bilinear transform is employed to reduce the temporal redundancy of the video. The spatial redundancy of the motion compensated error images is reduced by a combination of Fractal and DCT. It is shown that in the objects boundaries of the motion compensated error image Fractal outperforms DCT, while in the smooth areas DCT is betterthan Fractal. A hybrid combination of Fractal and DCT gives the best result. The performance of this hybrid codec with geometrically transformed motion compensation is compared against the H.261 standard video codec at 64 kbit/s.

State-of-the art approaches towards stereo image coding exploit interview redundancy by employing block-matching methods for disparity estimation and compensation. However, the efficiency of these methods is affected by mismatched areas, due to occlusions, brightness variations, or perspective distortion between objects of the two views. In this paper we present a new prediction scheme for stereo image coding, that combines an implicit disparity estimation method, with an adaptive least squares (LS)-based filtering. The Multidimensional Multiscale Parser image coding algorithm was used to evaluate the efficiency of the proposed scheme. Experimental results demonstrate the advantage of LS prediction in stereo image coding. Furthermore, the rate-distortion performance of the MMP based stereo encoder is well above that of the state-of-the-art H.264/AVC Stereo Profile, especially at medium and high bit rates.

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 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 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.

The Multidimensional Multiscale Parser (MMP) algorithm has been proposed recently as a universal data coding method. MMP has proved to be a very powerful coding method for images, as for other types of signals. Experimental tests showed that MMP is able to achieve better results than the traditional transform-based image coding methods, particularly for images that do not have a low-pass nature. These promising results motivated the use of MMP for residual error encoding in hybrid video coding algorithms. This paper presents the first results of these experiments, performed using a H.264/AVC based video encoder, but using MMP to encode the motion compensated residual data, for the P and B slices. Experimental results show that, even in this not fully optimised version, this method is able to achieve an approximately equivalent performance to the H.264/AVC. This demonstrates that MMP is an alternative to the transform-quantisation paradigm for hybrid video coding that is worth investigating.

This paper presents a method to recover lost blocks in depth maps affected by data loss in 3D image/video communications over error prone networks. The proposed method relies on the color image for accurate reconstruction of the lost contour segments in the corresponding depth map areas. Such reconstructed depth map contours are then used as boundaries at different depth planes to recover the missing depth values through weighted interpolation. The method performance is evaluated by the objective quality (PSNR) of the synthesised views. Images decoded with the reconstructed depth maps are compared with those of the reference method. The proposed method exhibits PSNR gains up to 1.49dB higher than the reference one, and a better performance is consistently achieved for different 3D image content.

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 [1], 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 1.5dB. 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.

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.

Sérgio Faria, that trusted me throughout the years. I'm thankful for their guidance and friendship shown inside and outside of our working environment. To Instituto de Telecomunicações for the excellent working conditions. To my research group colleagues Luís Lucas, Gilberto Jorge, João Santos, André Guarda, Filipe Gama and Sylvain Marcelino, for the companionship and for creating such a great working environment. A special thanks to Luís Lucas and Gilberto Jorge for helping me understand the HEVC code and working with linux. To my family, who have always supported and believed in me. To Tânia, for the unconditional love and patience, mainly when this work didn't let us be together as much as we would like.

The importance to develop more efficient 3D and multiview data representation algorithms results from the recent market growth for 3D video equipments and associated services. One of the most investigated formats is video+depth which uses depth image based rendering (DIBR) to combine the information of texture and depth, in order to create an arbitrary number of views in the decoder. Such approach requires that depth information must be accurately encoded. However, methods usually employed to encode texture do not seem to be suitable for depth map coding. In this paper we propose a novel depth map coding algorithm based on the assumption that depth images are piecewise-linear smooth signals. This algorithm is designed to encode sharp edges using a flexible dyadic block segmentation and hierarchical intra-prediction framework. The residual signal from this operation is aggregated into blocks which are approximated using linear modeling functions. Furthermore, the proposed algorithm uses a dictionary that increases the coding efficiency for previously used approximations. Experimental results for depth map coding show that synthesized views using the depth maps encoded by the proposed algorithm present higher PSNR than their counterparts, demonstrating the method's efficiency.

Recently, touchless multiview fingerprinting technology has been proposed as an alternative to overcome the intrinsic problems of traditional contact-based systems. Nevertheless, compression of this kind of signal has not been fully evaluated and standardized. This paper investigates the comparative performance of several encoders for this data, namely WSQ, JPEG2000, H.264/AVC and MMP. Experimental results show that WSQ encoder, which is the current compression standard for contactbased fingerprints, is objectively outperformed by all others. In particular, MMP, which achieved the best results, outperforms WSQ by up to 4 dB.

Template matching (TM) has been originally proposed as a texture synthesis tool. However, it has been successfully exploited for spatial and inter-frame prediction in video coding. In this paper we investigate the use of TM prediction for stereo image coding. In order to efficiently encode stereo images, the TM algorithm was optimized for stereo disparity prediction. Additionally, we have used the state-of-the-art pattern matching based algorithm, the Multidimensional Multiscale Parser (MMP), to encode images predicted with the proposed scheme. Experimental results of the developed stereo image encoder show that TM is able to efficiently exploit the redundancy between stereo views. The use of the new prediction method with MMP also achieves important coding gains over the stateof-the-art transform based H.264/AVC standard, for stereo image coding.

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.

Light field imaging is a promising new technology that allows the user not only to change the focus and perspective after taking a picture, as well as to generate 3D content, among other applications. However, light field images are characterized by large amounts of data and there is a lack of coding tools to efficiently encode this type of content. Therefore, this paper proposes the addition of two new prediction tools to the HEVC framework, to improve its coding efficiency. The first tool is based on the local linear embedding-based prediction and the second one is based on the self-similarity compensated prediction. Experimental results show improvements over JPEG and HEVC in terms of average bitrate savings of-71.44% and-31.87%, and average PSNR gains of 4.73dB and 0.89dB, respectively.

This paper proposes a two-stage high order intra block prediction method for light field image coding. This method exploits the spatial redundancy in lenslet light field images by predicting each image block, through a geometric transformation applied to a region of the causal encoded area. Light field images comprise an array of micro-images that are related by complex geometric transformations that cannot be efficiently compensated by state-of-the-art image coding techniques, which are usually based on low order translational prediction models. The two-stage nature of the proposed method allows to choose the order of the prediction model most suitable for each block, ranging from pure translations to projective or bilinear transformations, optimized according to an appropriate rate-distortion criterion. The proposed higher order intra block prediction approach was integrated into an HEVC codec and evaluated for both unfocused and focused light field camera models, using different resolutions and microlens arrays. Experimental results show consistent bitrate savings, which can go up to 12.62%, when compared to a lower order intra block prediction solution and 49.82% when compared to HEVC still picture coding.