3D Gaussian splats (3DGS) have rapidly gained traction as a 3D scene representation technique that enables efficient real-time rendering and highfidelity novel view synthesis. This paper reports on ongoing MPEG GSC efforts conducted jointly by the MPEG Video Coding group (WG 4) and the Coding of 3D Graphics and Haptics group (WG 7) to define a practical and interoperable compression framework for 3DGS. MPEG GSC is planning GSC standardization with short-term and long-term timelines to address market requirements. The short-term objective is to standardize coding tools that build on proven MPEG ecosystems while introducing only the minimal set of extensions, syntax, and processing required for INRIA-3DGS format (referred to in MPEG as I-3DGS). In the long-term, MPEG is also investigating broader alternatives for 3DGS representation and compression, including approaches that integrate training during compression, collectively referred to as Alternative-3DGS (A-3DGS). More specifically, this paper focuses on the I-3DGS and explores both geometrybased and video-based coding frameworks within MPEG GSC.

For a V-PCC1system to be able to reconstruct a single instance of the point cloud one V-PCC unit must be transferred to the 3D point cloud reconstruction module. It is however required that all the V-PCC components i.e. occupancy map, geometry, atlas and attribute to be temporally aligned. This, in principle, could pose a challenge since the temporal structures of the decoded sub-bitstreams are not coherent across V-PCC sub-bitstreams. In this paper we propose an output delay adjustment mechanism for the decoded V-PCC sub-bitstreams to provide synchronized V-PCC components input to the point cloud reconstruction module.

This paper presents a liveness detection method based on texture descriptors and artificial neural networks, whose objective is to identify potential attempts of spoofing attacks against touchless fingerprinting devices. First, a database was created. It comprises a set of 400 images, from which 200 represent real fingers and 200 represent fake fingers made of beeswax, corn flour play dough, latex, silicone and wood glue, 40 samples each. The artificial neural network classifier is trained and tested in 7 different scenarios. In Scenario 1, there are only two classes, “real finger” and “fake finger”. From Scenarios 2 to 6, six classes are used, but classification is done considering the “real finger” class and each one of the five “fake finger” classes, separately. Finally, in Scenario 7, six classes are used and the classifier must indicate to which of the six classes the acquired sample belongs. Results show that the proposed method achieves its goal, since it correctly detects liveness in almost 100% of cases.

Touchless multiview fingerprinting technology has been proposed as an alternative to overcome intrinsic problems of traditional touchbased systems. However, if one wants to benefit from the advantages presented by touchless scanners, the captured images must be processed in order to become compatible to touchbased systems. This paper proposes a two-step solution to the touchless-to-touch compatibility problem. First, it reproduces the texture of touchbased acquisition; and second, it performs a geometric transformation in order to approximate the nail-to-nail touchbased enrollment process. Two experiments were proposed, one to evaluate the quality of the processed images, another one to estimate the EER (equal error rate) for a set of 200 fingerprints (100 fingers, 2 images per finger). Results show that 90% of the images presented good, very good or excellent scores according to NFIQ. In addition, the observed EER was approximately 4%, demonstrating the viability of the proposed method.

The emerging video coding standard, HEVC, was developed to replace the current standard, H.264/AVC. However, in order to promote inter-operability with existing systems using the H.264/AVC, transcoding from H.264/AVC to the HEVC codec is highly needed. This paper presents a transcoding solution that uses machine learning techniques in order to map H.264/AVC macroblocks into HEVC coding units (CUs). Two alternatives to build the machine learning model are evaluated. The first uses a static training, where the model is built offline and used to transcode any video sequence. The other uses a dynamic training, with two well-defined stages: a training stage and a transcoding stage. In the training stage, full re-encoding is performed while the H.264/AVC and the HEVC information are gathered. This information is then used to build a model, which is used in the transcoding stage to classify the HEVC CU partitioning. Both solutions are tested with well-known video sequences and evaluated in terms of rate-distortion (RD) and complexity. The proposed method is on average 2.26 times faster than the trivial transcoder using fast motion estimation, while yielding a RD loss of only 3.6% in terms of bitrate.

This paper proposes a hybrid pattern matching/transform-based compression method for scanned documents. The idea is to use regular video interframe prediction as a pattern matching algorithm that can be applied to document coding. We show that this interpretation may generate residual data that can be efficiently compressed by a transform-based encoder. The efficiency of this approach is demonstrated using H.264/advanced video coding (AVC) as a high-quality single and multipage document compressor. The proposed method, called advanced document coding (ADC), uses segments of the originally independent scanned pages of a document to create a video sequence, which is then encoded through regular H.264/AVC. The encoding performance is unrivaled. Results show that ADC outperforms AVC-I (H.264/AVC operating in pure intramode) and JPEG2000 by up to 2.7 and 6.2 dB, respectively. Superior subjective quality is also achieved.

This paper proposes a hybrid pattern matching/transform-based compression engine for scanned compound documents. The novelty of this approach is demonstrated by using a modified version of the HEVC (High Efficiency Video Coding) Test Model as a compound document compressor, here conveniently referred to as HEDC (High Efficiency Document Coder). The proposed method uses segments of a document to create a video sequence, which is then encoded by HEDC. The idea is to explore interframe prediction as a pattern matching algorithm for coding units pre-classified as text; and intraframe prediction for coding units pre-classified as image. Results show that HEDC outperforms AVC-I, HEVC-I (H.264/AVC and HEVC operating in pure intra mode), H.264/AVC and JPEG2000 by up to 3.3, 2.5, 1.7 and 5 dB, respectively. Furthermore, for most documents the proposed method yields practically the same rate-distortion performance as regular HEVC, but is approximately 5% to 20% faster due to a pre-classification algorithm that prevents it of performing all possible inter/intra prediction tests for each prediction unit.

This paper proposes a hybrid approximate pattern matching/transform-based compression engine. The idea is to use regular video interframe prediction as a pattern matching algorithm that can be applied to document coding. We show that this interpretation may generate residual data that can be efficiently compressed by a transform-based encoder. The novelty of this approach is demonstrated by using H.264/AVC, the newest video compression standard, as a high quality book compressor. The proposed method uses segments of the originally independent scanned pages of a book to create a video sequence, which is encoded through regular H.264/AVC. Results show that the proposed method outperforms AVC-I (H.264/AVC operating in pure intra mode) and JPEG2000 by up to 4 dB and 7 dB, respectively. Superior subjective quality is also achieved.

The Mixed Raster Content (MRC) document compression is a well documented standard. Its efficiency for representing sharp text and graphics over a background has been extensively presented. Scanned documents, however, are difficult to be dealt with because of soft transitions. In one of our previous works we presented a pre/post-processing algorithm for MRC compression of scanned images that sharpens the edges, before compression, and softens it again, after reconstruction. The present paper uses the same basic idea but describes three new features that improve, in a rate-distortion sense, the compression performance. First, the real bitrate is used to determine the pre/post-processing parameters. Second, the mask layer and the edge sharpening/softening map have their quality improved and, third, layer resolution change has been explored. Experimental results show that the method not only provides higher subjective quality, but it can yield up to 2.5 dB gains in PSNR against single coder approaches, in the compression ranges of interest.

The mixed raster content (MRC) document compression standard (ITU T.44) specifies a multi-layer multi-resolution representation of a compound document. The model is very efficient for representing sharp text and graphics onto a background. However, since the mask layer is binary, it is difficult to deal with scanned data and soft edges. The edge transitions do not fully belong to the foreground neither to the background, and cause some "halo" to the object edges using the MRC model. This paper presents an algorithm that builds an edge sharpening map and iteratively parameterizes the original edge "softness" at the encoder. The generated map and the "softness" parameters are, then, used to reconstruct the original soft edges at the decoder. Experimental results are presented, showing that the method can yield 1.5 dB gains in PSNR, in the compression ranges of interest.

In this paper we explore H.264/AVC operating in intraframe mode to compress a mixed image, i.e. composed of text, graphics and pictures. Even though mixed contents (compound) documents usually require the use of multiple compressors, we apply a single compressor for both text and pictures. For that, distortion is taken into account differently between text and picture regions. Our approach is to use a segmentation-driven adaptation strategy to change the H.264/ AVC quantization parameter on a macroblock by macroblock basis, i.e. we deviate bits from pictorial regions to text in order to keep text edges sharp. We show results of a segmentation driven quantizer adaptation method applied to compress documents. Our reconstructed images have better text sharpness compared to straight unadapted coding, at negligible visual losses on pictorial regions. Our results also highlight the fact that H.264/AVC-INTRA outperforms coders such as JPEG-2000 as a single coder for compound images.

In this paper, we explore H.264/AVC operating in intraframe mode to compress a mixed image, i.e., composed of text, graphics, and pictures. Even though mixed contents (compound) documents usually require the use of multiple compressors, we apply a single compressor for both text and pictures. For that, distortion is taken into account differently between text and picture regions. Our approach is to use a segmentation-driven adaptation strategy to change the H.264/AVC quantization parameter on a macroblock by macroblock basis, i.e., we deviate bits from pictorial regions to text in order to keep text edges sharp. We show results of a segmentation driven quantizer adaptation method applied to compress documents. Our reconstructed images have better text sharpness compared to straight unadapted coding, at negligible visual losses on pictorial regions. Our results also highlight the fact that H.264/AVC-INTRA outperforms coders such as JPEG-2000 as a single coder for compound images.