3D Gaussian splatting enables real-time, photo-realistic scene rendering but is challenged by redundant, high-dimensional primitive data. To make immersive AR/VR and volumetric communication practical, compression must address both temporal and attribute redundancy. We introduce a streamlined inter-prediction coding pipeline that predicts each P-frame's color and geometry attributes from a nearby reference using a lightweight hybrid predictor, combining bilateral filtering and K-nearest-neighbor feature transfer, and encodes only residuals. Our approach targets the most rate-critical channels by applying codebook-based vector quantization to spherical harmonic components, while retaining efficient scalar quantization for other attributes. All quantized streams and codebooks are entropy coded. The proposed method achieves an average 89.1 % reduction in BD-rate and a 12.39 dB increase in BD-PSNR over GPCCv1, and outperforms previous SOTA interGS by 18.6 % in BD-rate and 1.81 dB in BD-PSNR. With practical decoding speeds (0.605 seconds per frame), interGS-Lite preserves real-time rendering and has potential for streaming and AR/VR applications, delivering consistent bitrate reductions with high visual quality.

A point cloud is a 3D data representation that presents unique challenges due to its large volume, high dimensionality, and lack of structure. This work introduces FDIP-PCAC, a$F$requency$D$omain$I$nter$P$rediction and Compensation system for dynamic point cloud attribute compression. The method combines deep learning, geometry-driven modeling, and Graph Fourier Transform (GFT) within a five-phase encoder–decoder framework. Initially, the reference and target point cloud frames are partitioned into corresponding nodes using a binary tree. In Phase 1, each target node identifies its K-nearest reference nodes based on the geometry centroid. Phase 2 computes the GFT latent representation of these selected reference nodes. Phase 3 and 4 transfer the RGB colors from matched reference nodes to target nodes, generating Predicted Target Node and Second-Stage Predicted Target Node, respectively. In phase 5, the neural network-based FreqNeRF predicts the GFT latent representation of target nodes. The proposed system achieves superior compression and reconstruction performance compared to G-PCC across diverse datasets. It delivers average BD-rate reductions of 65.89% against RAHT-v30, 63.61% against PredLift-v30, 67.59% against RAHT-v23 and 66.82% against PredLift-v23.

Current video-based GS compression methods rely on using Parallel Linear Assignment Sorting (PLAS) to convert 3D GS into smooth 2D maps, which are computationally expensive and time-consuming, limiting the application of GS on lightweight devices. In this paper, we propose a Lightweight 3D Gaussian Splatting (GS) Compression method based on Video codec (LGSCV). First, a two-stage Morton scan is proposed to generate blockwise 2D maps that are friendly for canonical video codecs in which the coding units (CU) are square blocks. A 3D Morton scan is used to permute GS primitives, followed by a 2D Morton scan to map the ordered GS primitives to 2D maps in a blockwise style. However, although the blockwise 2D maps report close performance to the PLAS map in high-bitrate regions, they show a quality collapse at medium-to-low bitrates. Therefore, a principal component analysis (PCA) is used to reduce the dimensionality of spherical harmonics (SH), and a MiniPLAS, which is flexible and fast, is designed to permute the primitives within certain block sizes. Incorporating SH PCA and MiniPLAS leads to a significant gain in rate-distortion (RD) performance, especially at medium and low bitrates. MiniPLAS can also guide the setting of the codec CU size configuration and significantly reduce encoding time. Experimental results on the MPEG dataset demonstrate that the proposed LGSCV achieves over 20% RD gain compared with state-of-the-art methods, while reducing 2D map generation time to approximately 1 second and cutting encoding time by 50%. The code is available at https://github.com/Qi-Yangsjtu/LGSCV.

Most existing 3D Gaussian Splatting (3DGS) compression schemes focus on producing compact 3DGS representation via implicit data embedding. They have long encoding and decoding times and highly customized data format, making it difficult for widespread deployment. This paper presents a new 3DGS compression framework called HybridGS, which takes advantage of both compact generation and standardized point cloud data encoding. HybridGS first generates compact and explicit 3DGS data. A dual-channel sparse representation is introduced to supervise the primitive position and feature bit depth. It then utilizes a canonical point cloud encoder to carry out further data compression and form standard output bitstreams. A simple and effective rate control scheme is proposed to pivot the interpretable data compression scheme. HybridGS does not include any modules aimed at improving 3DGS quality during generation. But experiment results show that it still provides comparable reconstruction performance against state-of-the-art methods, with evidently faster encoding and decoding speed. The code is publicly available at https://github.com/Qi-Yangsjtu/HybridGS .

Immersive visual communication has many important applications and Gaussian Splatting (GS) is a recent breakthrough that uses learnable geometry and color representation to capture 3D world with a very efficient parallelizable rendering pipeline. However, the compression of GS data still lacks efficiency and is quite complex in computation which prevents its adoption and deployment as a streaming solution in the real world. In this work, we develop a lightweight scalable GS coding scheme that exploits the correlation between adjacent quality layers and come up with a lightweight novel prediction and residual coding scheme that creates layered representation and is friendly to the MPEG DASH-like receiver-driven scalable streaming solutions. Simulation results demonstrate the efficiency of the proposed compression solution, as well as low latency/complexity in the decoding and rendering process. To the best of our knowledge, this is the first high-efficiency and low-complexity scalable GS coding solution that can be deployed with the existing MPEG DASH framework.

Dynamic multi-view video is increasingly important for applications in VR/AR, telepresence, and volumetric streaming. 3D Gaussian Splatting (3DGS) has emerged as a powerful learnable representation enabling real-time rendering of dynamic scenes, but each frame typically contains hundreds of thousands of Gaussian primitives, leading to prohibitive storage and transmission costs when compressed independently. To address this, we introduce InterGS, the inter-prediction framework designed specifically for Gaussian splatting sequences. InterGS exploits temporal coherence by predicting each P-frame’s high-dimensional color and geometry attributes from a preceding I-frame using a lightweight hybrid predictor that unifies bilateral filter prediction and nearest-neighbor feature prediction. A simple adaptive selector chooses the most accurate prediction for each Gaussian, and only the resulting prediction residuals are quantized and entropy-coded. Overall coding scheme is light weight in computational complexity, and highly parallelizeable for potential real time streaming applications. On three sequences from the MPEG-GSC benchmark, InterGS achieves an average BD-rate reduction of 88.31%, and on two AVS sequences it delivers a 64.47% reduction. These results demonstrate that InterGS offers a practical, high-efficiency solution for dynamic Gaussian splatting compression.

A point cloud is a standard 3D data representation that poses challenges due to its large volume, high dimensionality, and unstructured nature. This paper introduces the Graph Fourier Transform Neural Radiance Field for Dynamic 3D Point Cloud Attribute Coding, termed GeRF3D-PCAC. We present a geometry-guided compression method for dynamic point cloud attributes using a four-step encoder-decoder framework. First, a binary tree partitions both reference(Ft) and target(Ft+1) point cloud frames, and each target partition locates its top-k nearest reference partitions using centroid-based KNN search. Second, the optimal reference node is chosen by comparing the Graph Fourier Transform (GFT) based latent representations of the target node and its candidates using mean squared error. Third, the target node is predicted by transferring RGB colors from geometry-matching points in the reference frame to the target geometry. Finally, GeRF3D predicts the GFT latent representation of target nodes, and three bitstreams, such as reference indices, quantized GeRF3D weights, and Y-channel residuals in the frequency domain, are generated and transmitted to the decoder. Our method outperforms G-PCC-PredLift-v23, G-PCC-RAHT-v23, and Unicorn in both compression and reconstruction across benchmark datasets.

High-fidelity 3D representations of objects and scenes can be obtained with point clouds, but dealing with their massive data sizes can be difficult. This data is efficiently compressed via MPEG’s Geometry-based Point Cloud Compression (G-PCC), which makes it manageable and useful for real-world applications. One major drawback, though, is that decoding introduces coding artifacts that cause the reconstructed point cloud to appear blocky. In this paper, we present a new approach to attribute learning in point clouds leveraging sparse convolution, that effectively deals with the non-uniformity and sparsity of these data structures.

Transformer-based Spatial and Frequency-Decomposed Feature Fusion Network (TSF-Net) exhibited great potential as a learned in-loop filter in Versatile Video Coding (VVC). Utilizing a channel-wise transformer for pixel and frequency-decomposed feature fusion in a multi-scale deep-learning setup, TSF-Net achieved remarkable success in removing video compression artifacts. In this article, considering the potential of TSF-Net, we extend this work to the 3D domain of point clouds and propose a new framework called TSF-Net3D. More specifically, we incorporate sparse convolution (SparseConv) to process point clouds and implement TSF-Net3D as a post-processing block in Geometry-based Point Cloud Compression (G-PCC) to enhance the quality of color attribute in the reconstructed frame. Implementation-wise, TSF-Net3D differs from TSF-Net in two fronts: (1) TSF-Net3D does not utilize frequency-decomposed information but rather pixel information only; (2) TSF-Net3D extends point cloud processing in three scales with two-level feature fusion, unlike TSF-Net, which processes features at only two scales with single-level feature fusion. We evaluate TSF-Net3D on the 8 iVFBv 2 dataset, and our experimental results demonstrate that our proposed method achieves a significant YUV Bjøntegaard Delta (BD) - bitrate saving of up to -13.12% over the G-PCC(TMC13v21) RAHT baseline while also outperforming other state-of-the-art methods.

A point cloud (PC) is a popular 3D data representation that poses challenges due to its size, dimensionality, and unstructured nature. This paper introduces the Residual Neural Radiance Field for Point Cloud Attribute Coding (ResNeRFPCAC), a novel approach for point cloud attribute compression. ResNeRF-PCAC combines sparse convolutions with neural radiance fields, to create a highly efficient attribute coding solution. It initially downscales the point cloud to generate a coarse thumbnail point cloud and encodes it using the G-PCC attribute encoder. The thumbnail PC is upsampled using a super-resolution network to generate a recolored PC. Color attribute residuals are then computed between the original and the super-resolved recolored PC. A ResNeRF network is employed to predict these residuals. The trained ResNeRF weights are compressed into a bitstream. The thumbnail bitstream and the compressed model weights are then transmitted to the decoder. Sparse convolution-based super-resolving network weights are shared and common across all content and need not to be signaled. Experiments on the MPEG-8i dataset demonstrate superior performance in terms of reconstruction quality and compression ratio compared to G-PCCRAHT and G-PCC-Predlift for both v14 and v21.

The rapid development of 3D modeling and computer vision has made point cloud data essential across various industries. Effective processing, transmission, and storage of these point clouds require innovative filtering and compression methods. Unlike traditional image and video media, point clouds are sparse and non-uniformly sampled, posing unique compression challenges. In this paper, we introduce a novel approach for deblocking and denoising point clouds using multi-scale sparse convolution-based attribute learning in the spectral domain. Our method leverages concurrent voxelized feature embedding for efficiency and utilizes sparsity to build a deeper learning structure. By employing the Graph Fourier Transform (GFT), we better understand spectral patterns and spatial correlations of attributes. Experiments show that our GFT latent representation significantly enhances reconstruction quality, achieving an 18.5% Y-BD rate reduction compared to GPCC TMC13v14 anchors. This extends our previous work, MUSCON, the first SparseConv-based multi-scale attribute upsampling solution for deblocking.

Upsampling a sparse point cloud is a common operation in various applications, such as high-quality reconstruction, rendering. Point-cloud geometry upsampling has been extensively studied for this purpose. However, when a sparse point cloud includes attributes, such as color, their upsampling must also be carried out in addition to geometry. Despite the apparent need of such work, not a lot of effective solutions are developed that can work with real world large scale point cloud. In this work, we propose a novel solution called PointCU, which is a sparse convolution learning-based Point cloud Color Upsampling method that enables high-fidelity dense point color reconstruction from sparse point color. The proposed method first prepares multiple representations of a dense point cloud through voxelization at different scales, then transfers the color from sparse to the newly created point clouds including the dense point cloud itself through devoxelization. Then by learning feature on these multiple point cloud representation through sparse convolution neural network (SparseCNN) while also expanding and fusing the feature to higher scale, PointCU achieves an excellent color super-resolving capability. Our experimental results on four times (4x) and eight times (8x) upsampling tasks demonstrate that the color upsampling performance of the proposed method is superior to the previous known color upsampling schemes by a large margin.

Block-based video codecs such as Versatile Video Coding (VVC)/H.266, High Efficiency Video Coding (HEVC)/H.265, Advanced Video Coding (AVC)/H.264 etc. inherently introduces compression artifacts. Although these codecs have in-loop filters to correct these distortions, they are not always effective due to the complexity of the noise. Recently, deep-learning approaches emerged as a promising solution for in-loop filtering. However, most of the previous approaches were designed solely for learning from images and neglected the high-frequency signals present in the reconstructed video frames. Furthermore, some previous methods employed a multi-level feature-extraction and feature-fusion strategy to enhance performance. However, they utilized complex feature-extractors while relying on naive feature-fusion methods. In this article, we propose a novel framework calledTSF-Net, which jointly learns from both the pixel (spatial) and frequency-decomposed information and through powerful capability of a channel-wise transformer, it fuses both these information to improve performance. Our approach deviates from previous approaches by employing a simple feature-extractor coupled with an advanced transformer-based feature-fusion module. Simultaneously,TSF-Netintroduces a few fundamental modifications in the multi-head self-attention module of the channel-wise transformer to make it computationally efficient. Our experimental results show that the proposedTSF-Netachieves a Bjøntegaard Delta (BD) - bitrate saving of up to 10.258% for the luma (Y) component under all-intra (AI) profile outperforming the VVC baseline and other state-of-the-art methods. Moreover, the proposedTSF-Netwith an efficient channel-wise transformer is twice as efficient asTSF-Netwith a vanilla channel-wise transformer.

Efficient point cloud compression is essential for applications like virtual and mixed reality, autonomous driving, and cultural heritage. This paper proposes a deep learning-based inter-frame encoding scheme for dynamic point cloud geometry compression. We propose a lossy geometry compression scheme that predicts the latent representation of the current frame using the previous frame by employing a novel feature space inter-prediction network. The proposed network utilizes sparse convolutions with hierarchical multiscale 3D feature learning to encode the current frame using the previous frame. The proposed method introduces a novel predictor network for motion compensation in the feature domain to map the latent representation of the previous frame to the coordinates of the current frame to predict the current frame's feature embedding. The framework transmits the residual of the predicted features and the actual features by compressing them using a learned probabilistic factorized entropy model. At the receiver, the decoder hierarchically reconstructs the current frame by progressively rescaling the feature embedding. The proposed framework is compared to the state-of-the-art Video-based Point Cloud Compression (V-PCC) and Geometry-based Point Cloud Compression (G-PCC) schemes standardized by the Moving Picture Experts Group (MPEG). The proposed method achieves more than 88% BD-Rate (Bjøntegaard Delta Rate) reduction against G-PCCv20 Octree, more than 56% BD-Rate savings against G-PCCv20 Trisoup, more than 62% BD-Rate reduction against V-PCC intra-frame encoding mode, and more than 52% BD-Rate savings against V-PCC P-frame-based inter-frame encoding mode using HEVC. These significant performance gains are cross-checked and verified in the MPEG working group.

Dense photorealistic point clouds can depict real-world dynamic objects in high resolution and with a high frame rate. Frame interpolation of such dynamic point clouds would enable the distribution, processing, and compression of such content. In this work, we propose a first point cloud interpolation framework for photorealistic dynamic point clouds. Given two consecutive dynamic point cloud frames, our framework aims to generate intermediate frame(s) between them. The proposed deep learning framework has three major components: the encoder module, the fusion network, and the multi-scale point cloud synthesis module. The encoder module extracts multi-scale features from two consecutive frames. The fusion network employs a novel 4D feature learning technique to merge the multi-scale features from consecutive frames. Finally, the multi-scale point cloud synthesis module hierarchically reconstructs the interpolated point cloud intermediate frame at different resolutions. We evaluate our framework on high-resolution point cloud datasets used in MPEG, JPEG Pleno, and AVS standards. The quantitative and qualitative results demonstrate the effectiveness of the proposed method.

Versatile Video Coding (VVC)/H.266 is currently the state-of-the-art video coding standard with significant improvement in coding efficiency over its predecessor High Efficiency Video Coding (HEVC)/H.26S. Nonetheless, VVC is also block-based video coding technology where decoded pictures contain compression artifacts. In VVC, in-loop filters serve to suppress these compression artifacts. In this paper, convolution neural network (CNN) is utilized to better facilitate the suppression of compression artifacts over VVC. Nonetheless, our approach has uniqueness in obtaining better features by exploiting locally correlated spatial features in the pixel domain as well as long-range correlated spectral features in the discrete cosine transform (DCT) domain. In particular, we utilized CNN-features from DCT transformed input to extract high-frequency components and induce long-range correlation into the spatial CNN-features by employing multi-stage feature fusion. Our experimental result shows that the proposed approach achieves significant coding improvements up to 9.70% on average Bjantegaard Delta (BD)-Bitrate savings under AI configurations for luma (Y) components.

Due to the increased popularity of augmented and virtual reality experiences, the interest in capturing high-resolution real-world point clouds has never been higher. Loss of details and irregularities in point cloud geometry can occur during the capturing, processing, and compression pipeline. It is essential to address these challenges by being able to upsample a low Level-of-Detail (LoD) point cloud into a high LoD point cloud. Current upsampling methods suffer from several weaknesses in handling point cloud upsampling, especially in dense real-world photo-realistic point clouds. In this paper, we present a novel geometry upsampling technique, PU-Dense, which can process a diverse set of point clouds including synthetic mesh-based point clouds, real-world high-resolution point clouds, real-world indoor LiDAR scanned objects, as well as outdoor dynamically acquired LiDAR-based point clouds. PU-Dense employs a 3D multiscale architecture using sparse convolutional networks that hierarchically reconstruct an upsampled point cloud geometry via progressive rescaling and multiscale feature extraction. The framework employs a UNet type architecture that downscales the point cloud to a bottleneck and then upscales it to a higher level-of-detail (LoD) point cloud. PU-Dense introduces a novel Feature Extraction Unit that incorporates multiscale spatial learning by employing filters at multiple sampling rates and receptive fields. The architecture is memory efficient and is driven by a binary voxel occupancy classification loss that allows it to process high-resolution dense point clouds with millions of points during inference time. Qualitative and quantitative experimental results show that our method significantly outperforms the state-of-the-art approaches by a large margin while having much lower inference time complexity. We further test our dataset on high-resolution photo-realistic datasets. In addition, our method can handle noisy data well. We further show that our approach is memory efficient compared to the state-of-the-art methods.

Federated Learning (FL), a distributed machine learning architecture, emerged to solve the intelligent data analysis on massive data generated at network edge-devices. With this paradigm, a model is jointly learned in parallel at edge-devices without needing to send voluminous data to a central FL server. This not only allows a model to learn in a feasible duration by reducing network latency but also preserves data privacy. Nonetheless, when thousands of edge-devices are attached to an FL framework, limited network resources inevitably impose intolerable training latency. In this work, we propose model-update compression to solve this issue in a very novel way. The proposed method learns multiple Gaussian distributions that best describe the high dimensional gradient parameters. In the FL server, high dimensional gradients are repopulated from Gaussian distributions utilizing likelihood function parameters which are communicated to the server. Since the distribution information parameters constitute a very small percentage of values compared to the high dimensional gradients themselves, our proposed method is able to save significant uplink band-width while preserving the model accuracy. Experimental results validated our claim.

This paper presents the intra prediction and mode coding of the Versatile Video Coding (VVC) standard. This standard was collaboratively developed by the Joint Video Experts Team (JVET). It follows the traditional architecture of a hybrid block-based codec that was also the basis of previous standards. Almost all intra prediction features of VVC either contain substantial modifications in comparison with its predecessor H.265/HEVC or were newly added. The key aspects of these tools are the following: 65 angular intra prediction modes with block shape-adaptive directions and 4-tap interpolation filters are supported as well as the DC and Planar mode, Position Dependent Prediction Combination is applied for most of these modes, Multiple Reference Line Prediction can be used, an intra block can be further subdivided by the Intra Subpartition mode, Matrix-based Intra Prediction is supported, and the chroma prediction signal can be generated by the Cross Component Linear Model method. Finally, the intra prediction mode in VVC is coded separately for luma and chroma. Here, a Most Probable Mode list containing six modes is applied for luma. The individual compression performance of tools is reported in this paper. For the full VVC intra codec, a bitrate saving of 25% on average is reported over H.265/HEVC using an objective metric. Significant subjective benefits are illustrated with specific examples.

This article describes the main video coding technologies included in a joint proposal submitted by Qualcomm and Technicolor, in response to a Call for Proposals (CfP) issued by ITU-T SG16 WP3 Q.6 (VCEG) and ISO/IEC JTC1/SC29/WG11 (MPEG) in Oct. 2017. The proposal contains the majority of the tools that have been adopted into the Joint Exploration Model (JEM), developed in the exploratory phase that preceded the CfP. A flexible multi-tree type (MTT) block-partitioning scheme is proposed to extend the quadtree and binary tree (QTBT) based partitioning in JEM by including triple tree (TT) and asymmetric binary tree (ABT) partitions. In addition, several JEM tools in intra and inter prediction, transforms and arithmetic coding are modified, and new tools such as sign prediction and motion compensated padding are proposed. Objective standard dynamic range (SDR) gains of 43.1% and 15.5% in terms of average luma BD-rate improvement have been achieved for the CfP constraint set 1 (random-access configuration) relative to HEVC/H.265 (HM) and JEM anchors, respectively. For the CfP constraint set 2 (low-delay configuration), the average luma BD-rate improvements are 33.7% relative to the HM anchor and 12.7% relative to the JEM anchor. The proposed codec scored highly in both subjective evaluations and objective metrics and was among the best-performing CfP proposals.

This paper describes a scalable video codec that was submitted as a response to the joint call for proposals issued by ISO/IEC MPEG and ITU-T VCEG on HEVC scalable extension. The proposed codec uses a multi-loop decoding structure. Several inter-layer texture prediction methods are employed to remove the inter-layer redundancy. Inter-layer prediction is also used when coding enhancement layer syntax elements such as motion parameter and intra prediction mode, to further reduce bit overhead. Additionally, alternative transforms as well as adaptive coefficients scanning are used to code the prediction residues more efficiently. Experimental results are presented to demonstrate the effectiveness of the proposed scheme. When compared to HEVC single-layer coding, the additional rate overhead for the proposed scalable extension is 1.2% to 6.4% to achieve two layers of SNR and spatial scalability.

This paper describes the in-loop deblocking filter used in the upcoming High Efficiency Video Coding (HEVC) standard to reduce visible artifacts at block boundaries. The deblocking filter performs detection of the artifacts at the coded block boundaries and attenuates them by applying a selected filter. Compared to the H.264/AVC deblocking filter, the HEVC deblocking filter has lower computational complexity and better parallel processing capabilities while still achieving significant reduction of the visual artifacts.

We examine the bit rate variability-distortion (VD) curve of I, P, and B frames of MPEG-4 VBR encoded video sequences. We show that the concave VD curve shape at high compression ratios or large quantization scales, is influenced by both the texture and the motion information. We use linear and quadratic models for the texture and motion bits statistics and devise accurate VD curve models. The model parameters are obtained from statistics that are estimated from two encodings. This work extends our previous work on modeling the VD curve, which has applications for optimal statistical multiplexing of VBR streaming video

In this paper we elaborate on a recently proposed approach for scalable video-coding based on in-band prediction in the overcomplete wavelet domain. It is shown that, through a new calculation scheme for the level-by-level complete to overcomplete discrete wavelet transform (DWT) that exploits certain symmetries, important reductions in the multiplication budget are obtained in comparison to the fastest-known algorithm of the literature. Based on the derived overcomplete transform-domain coefficients, a pixel-accurate motion estimation and compensation (ME/MC) algorithm is proposed, which provides a hybrid-coding framework that supports full scalability in resolution, quality and frame rate. To give an indication of the coding performance of such a system, some preliminary results are reported.

A new in-band motion compensation algorithm for wavelet-based video coding is proposed: the bottom-up prediction algorithm (BUP). The BUP algorithm overcomes the periodic shift-invariance of the discrete wavelet transform (DWT) and is formalized into new prediction rules using filtering operations. BUP is based on the relationships between the subbands of the shifted input signal and the subbands of the non-shifted reference signal, whereby the number of shifts is limited by the periodic shift-invariance of the DWT. We derive the algorithm for the 1-D DWT with three decomposition levels. The combination of all prediction rules of the BUP algorithm defines a new transform: the bottom-up overcomplete DWT or BUP ODWT, which is shift-invariant. The BUP ODWT calculates the overcomplete subbands by applying the prediction rules to the critically sampled subbands of a wavelet-transformed image. The envisaged application for the BUP algorithm is spatially scalable video coding.

We study the compression performance of the quincunx discrete wavelet transform (DWT) and we compare it with the dyadic DWT in terms of rate-distortion. The 2D non-separable quincunx wavelet filters are designed by making use of the transformations of variables technique of Tay and Kingsbury (1993) starting from the 1D biorthogonal (9,7)-taps filters. The applied transformation functions are respectively based on the Kaiser and Chebyshev windows, and on the Lagrange halfband filters. The novelties of this work are in the evaluation of these filters for image compression and the usage of an embedded coder based on the quadtree approach for coding the quincunx subbands.

Perfect reconstruction, quality scalability, and region-of-interest coding are basic features needed for the image compression schemes used in telemedicine applications. This paper proposes a new wavelet-based embedded compression technique that efficiently exploits the intraband dependencies and uses a quadtree-based approach to encode the significance maps. The algorithm produces a losslessly compressed embedded data stream, supports quality scalability, and permits region-of-interest coding. Moreover, experimental results obtained on various images show that the proposed algorithm provides competitive lossless/lossy compression results. The proposed technique is well suited for telemedicine applications that require fast interactive handling of large image sets, over networks with limited and/or variable bandwidth.

This work proposes a new block based motion estimation and compensation technique applied on the detail images of the wavelet pyramidal decomposition. The algorithm uses two matching criteria, namely the absolute difference and the absolute sum. For a wavelet decomposed one-dimensional step function, it is shown that for odd translations of the step, the absolute sum reaches a smaller minimum than the absolute difference. We also derive in this case a constraint on the highpass filter coefficients so that a zero prediction error can be reached by using the absolute sum. Although this cannot be easily generalized for an arbitrary signal profile, experimental results obtained with photorealistic image sequences indicate that the prediction error can be reduced with respect to techniques that only use the absolute difference as matching criterion.