The increasing prevalence of ML in industrial environments is driven by the growing availability of userfriendly frameworks and industrial data. Manufacturing Execution Systems (MES) enabled easy data collection and utilization for decision support, namely for anomaly detection, quality control, or object detection/classification. However, models for new ML problems are often trained without regard for previous models or data, potentially wasting resources and hindering knowledge transfer. This is due to a lack of systematic methods for identifying and leveraging relevant prior knowledge. In this paper, we propose an approach designed to address this inefficiency by reusing previously trained models in new ML tasks. We reuse models based on data similarity metrics to create ensembles on-the-fly. This allows for accurate predictions on new data while minimizing the need for training from scratch. This approach has the potential to significantly reduce resource expenditure on data labeling and model training within industrial organizations.

This paper evaluates a new implementation of our Silhouette 3D (S3D) coder for lossless compression of point cloud geometry, which yields up to 18% improvement on compression efficiency over our previously reported results. With this it offers competitive performance compared to the state-of-the-art MPEG G-PCC, outperforming it for some sequences. Additionally, we propose and analyze two multithreaded algorithms based on the S3D coder, namely S3D-Subtree (S3D-S) and S3D-Subtree + TopTree (S3D-ST). We evaluate the performance of these algorithms from 2 to 128 threads so that the user can select the best tradeoff for the desired application. As expected, parallelization decreases compression efficiency, but both S3D-S and S3D-ST show minor penalties for a small number of threads, and S3D-ST offers better compression efficiency when a larger number is used.

Recently we have proposed a coding algorithm of point cloud geometry based on a rather different approach from the popular octree representation. In our algorithm, the point cloud is decomposed in silhouettes, hence the name Silhouette Coder, and context adaptive arithmetic coding is used to exploit redundancies within the point cloud (intra frame coding), and also using a reference point cloud (inter frame coding). In this letter we build on our previous work and propose a context selection algorithm as a pre-processing stage. With this algorithm, the point cloud is first parsed testing a large number of candidate context locations. The algorithm selects a small number of these contexts that better reflect the current point cloud, and then encode it with this choice. The proposed method further improves the results of our previous coder, Silhouette 4D, by $10 \%$, on average, on a dynamic point cloud dataset of the JPEG Pleno, and achieves bitrates competitive with some high quality lossy coders such as the MPEG G-PCC.

This paper proposes a lossy intra-frame coder of the geometry information of voxelized point clouds. Using an alternative approach to the widespread octree representation, this method represents the point cloud as an array of binary images. This algorithm works recursively using a dyadic decomposition that splits an interval of slices in two smaller intervals, depicting a binary tree traversal, and transmitting the occupancy information of each interval. The sequence of bi-level images are encoded in a lossless fashion until a fixed point in the tree, from where the algorithm “skips” the dyadic slicing and transmits all the k remaining slices as leaves of the tree, which are then encoded in a lossy fashion. The performance assessment shows that the proposed method outperforms state-of-the-art intra coders of lossy geometry for medium to higher bitrates on the public point cloud datasets tested.

In recent years image compression techniques based on deep learning have achieved great success and their performances are gradually reaching the methods crafted by experts, such as JPEG, WebP, and Better Portable Graphics (BPG). A technique that is fundamental for modern image and video codecs is intra prediction, which takes advantage of local redundancy to predict the pixels from previously encoded neighbors. In this paper, we use Convolutional Neural Networks (CNN) to develop a new intra-picture prediction mode. More specifically, we propose a multi-mode intra prediction approach that uses two CNN-based prediction modes and all intra modes previously implemented in the High Efficiency Video Coding (HEVC) standard. We also propose a bit allocation technique that increases the bitstream only if the reconstruction error is significantly reduced. Experimental results evince a significant and consistent performance increase compared to other approaches that use a similar backbone architecture, with 28% bitrate reduction compared to the baseline codec.

This paper presents a lossless inter geometry coder of voxelized point clouds. We build upon our previous work, extending the contexts used in the arithmetic coding to include voxels from a reference point cloud, hence the name, 4D contexts. We show that considering both 3D and 4D contexts leads to a substantial gain compared to considering each one on its own, and we also propose a fast decision method to avoid encoding each slice with both contexts. The proposed codec has the same complexity as our previous intra codec, but it shows an equal or superior performance for all point clouds tested. Results show that the proposed method outperforms all intra and inter state-of-the-art coders on the public available datasets tested.

This letter presents a lossless intra coder of the geometry information of voxelized point clouds. Instead of using the popular octree decomposition, the proposed method views the point cloud geometry as an array of bi-level images, and it is inspired by well-known techniques for coding this type of images. This array is encoded using a dyadic decomposition that recursively splits the array into two arrays of half its size, transmitting the occupancy information of each smaller array. Context adaptive arithmetic coding, using both 2D and 3D contexts, is used to achieve efficient compression. Results show that the proposed method outperforms all state-of-the-art intra coders on the public available point cloud datasets tested.

Point clouds are one of the most promising technologies for 3D content representation. Point clouds are a set of points described by their geometry information and additional attributes, of which colour is the most typical. When transmitting a point cloud, both geometry, colour and temporal information need to be transmitted, thus making compression efficiency key in exploiting this technology. In this paper, we propose a lossless intra coder of the geometry information of voxelized point clouds. Instead of using the popular octree approach, we use well-known techniques for coding of bi-level images, as well as a novel boolean decomposition method to further exploit the redundancy within the geometry. Results show that the proposed method outperforms other recent coders on the sequences tested.

Due to the large size of a light field image, compressing and transmitting the entire data to a client before rendering any image for observation would incur a significant startup delay. In response, in interactive light field streaming (ILFS) a server synthesizes and transmits a new viewpoint image as a combination of sub-aperture images (SAIs) per user request. However, in so doing the client relies entirely on the server for reconstruction of every requested image. In this paper, we extend a previous proposal of progressive light field data transmission strategy, where the client can incrementally learn SAIs over time. Specifically, requested focal-point images are synthesized using carefully chosen weighted linear combinations of SAIs, so that recovery of SAIs amounts to inversion of a lower-triangular weight matrix-a matrix structure that enables SAI recovery without amplifying quantization noise due to lossy image coding. We design an objective function to encourage specific combinations of SAIs to increase rank of the lower-triangular weight matrix for fast SAI recovery. This new proposal reduces the size of the initial user's cache and the total number of transmitted images compared to our previous work. Experimental results show that our scheme can outperform ILFS by up to 70% in terms of BD-rate.

Light field cameras capture the emanated light from a scene. This type of images allows for changing point of views or focal points by processing the captured information. Recently, a Progressive Light Field Communication (PLFC) was proposed. PLFC addresses an interactive Light Field (LF) streaming framework, where a client requests a certain view or focal point and a server synthesizes and transmits each requested image as a linear combination of Sub-Aperture Images (SAI). The main idea of PLFC is that as the virtual views are transmitted, the client gradually learns information about the LF, so eventually the client may posses enough information to locally create the virtual view at the required quality, avoiding the transmission of a new image. In order to PLFC work, an optimization algorithm which selects the SAIs that are used to create a certain virtual view is requested. Here, we improve over the previous PLFC proposal by presenting a method that focuses on a refinement algorithm for SAI selection, using dynamic Quantization Parameter (QP) during encoding, using an automatic method to determine the Lagrangian multiplier during optimization and modifying how the initial required cache is created. These proposed changes in the algorithm produce significant gains. The results shows gains up to 85.8% on BD-rate compared to trivial LF transmissions, whereas they're up to 32.8% compared to previous PLFC.

Light field (LF) imaging captures multiple intensities and directions of light per pixel during acquisition in a 3D scene, so that novel images of different viewpoints or focal points can be synthesized. However, transmitting all LF data before viewer observation incurs a large startup delay. To avoid such delay, we propose a new interactive LF streaming framework, where a client periodically requests viewpoint images, and in response a server synthesizes and transmits each requested image as a carefully chosen sparse linear combination of sub-aperture images. For each received synthesized image, the client “decodes” and recovers a new sub-aperture image using a cache of known sub-aperture images. As the cache of decoded sub-aperture images grows over time, the client becomes capable of synthesizing new view/focal point images, reducing overall transmission cost. Experimental results show that our proposed scheme can deliver synthesized images at high quality, even though only sparse sub-aperture images are used for synthesis. Moreover, compared with a scenario where the requested synthesized images are always transmitted, our proposed scheme achieves a significant reduction in accumulated rate when a sufficient number of images are transmitted.

Block-based motion estimation is the method of choice in most video codecs to exploit temporal redundancy for compression. Since true rate-distortion evaluation for every candidate block is usually impractical, simple estimates are used instead as a matching criterion, e.g., the Sum of Absolute Differences (SAD) between the target and the candidate blocks weighted by its respective motion vector cost in bits. We show that different matching criteria may differ not only in the quality of the resulting motion estimation but may actually offer diverse motion estimates that can be locally selected to better suit the local data characteristics. As a proof of concept, we propose the Double Matching Criteria Algorithm (DMCA), a two-pass algorithm which performs two independent motion estimations for each block with different matching criteria and then locally selects the better one in a true rate-distortion sense. The algorithm is easily parallelizable and out-of-the-box compliant to any coding standard with support for block-based motion compensation. We also propose the Total Absolute Deviation from the Mean (TADM) as a matching criterion to be used along with the SAD in the DMCA framework. Unlike the SAD, which measures the size of the residue in a sense, the TADM is a measure of its dispersion. For demonstration purposes, we implemented the DMCA with the SAD and the TADM as matching criteria in a modified HM reference software encoder for the HEVC standard. We observed significant BD-rate gains with a fully compliant HEVC stream.

Context - Software companies should track innovation as rigorously as core business operations. For that, the assessment of innovation projects is a critical process, in particular to make their innovation initiatives funded. Objective - In this article, we aim to evaluate the need for more practical measurement tools, by checking the agreement of very experienced analysts, from the industry, about the innovation degree of four actual software projects. Method - We conducted a survey with eight business analysts, using a combination of the Three Horizons Model and the Gartner's Hyper Cycle for emerging technologies as a frame of reference. Results - In general, the level of agreement about the innovation degree in the projects was very low. Looking at the cases in isolation, it is possible to suggest reasons for the low level of agreement between the evaluators. Conclusions - Our data support the fact that innovation is an activity difficult to characterize and even more difficult to measure, and the need for practices to achieve better intersubjective agreement for innovation assessment became evident in this work.

The flexible partitioning scheme and increased number of prediction modes in the High Efficiency Video Coding (HEVC) standard are largely responsible for both its high compression efficiency and computational complexity. Each frame in HEVC is partitioned in Coding Tree Units (CTUs) of fixed size, which are then recursively partitioned in Coding Units (CUs). In typical implementations, CUs in a CTU are visited from top to bottom at each level of recursion. In this paper, a different approach is used in which CUs in a CTU can be adaptively visited also in reverse order from bottom to top. Three novel algorithms to reduce complexity of HEVC depth selection, mode decision and inter-prediction are presented, based on this adaptive visiting order. Experimental results show that the proposed encoder achieves on average 38.2% speed-ups compared to fast reference HEVC implementations with pre-built speed-ups enabled, for very limited efficiency losses.

The flexible partitioning scheme and increased number of prediction modes in the high efficiency video coding (HEVC) standard are largely responsible for both its high compression efficiency and computational complexity. In typical HEVC encoder implementations, coding units (CUs) in a coding tree unit (CTU) are visited from top to bottom at each level of recursion to select the optimal coding configuration. In this paper, a novel approach is presented in which CUs in a CTU can be adaptively visited also ina reverse, bottom to top visiting order. This reverse CU (RCU) visiting order allows for different algorithmic optimizations for further complexity reduction of many HEVC encoding steps, especially under challenging conditions, such as highly textured or fast moving content. In particular, algorithms to reduce complexity of HEVC depth selection, mode decision, and inter-prediction are presented here based on the coding information obtained from higher depths when using the RCU visiting order. Experimental results show that enabling different stages of the proposed algorithm can achieve average speedups from 16.3% to 36.6% compared to fast reference HEVC implementation with pre-built speed-ups enabled (up to 51.2% in some cases), for 0.3% to 2.2% BD-rate penalty.

Typical H.265/High Efficiency Video Coding (HEVC) encoder implementations test a variety of prediction modes and select the optimal configuration for each block in terms of Rate-Distortion (RD) cost. A fast HEVC mode decision algorithm is proposed here referred to as Context Adaptive Mode Sorting (CAMS). The frequency of selection of modes and their RD costs are collected while encoding the training frames based on local parameters (the context). This information is then used to sort and restrict the prediction modes to test for each context, while the optimal mode found using CAMS on each CU is validated based on the RD cost distributions found during the training. Experimental results show that the method reduces total encoding time of fast HEVC implementations on average by 29.3%, at modest efficiency losses.

The High Efficiency Video Coding (HEVC) standard makes use of flexible partitioning to achieve high compression ratios. Each frame is divided in Coding Tree Units (CTUs) of fixed size, which are further partitioned into Coding Units (CUs) following a recursive quadtree structure. Typically, CUs at each level of recursion are tested to select the optimal coding configuration, hence this process is extremely demanding in terms of computational complexity. In this paper, a method to reduce complexity of HEVC quadtree configuration selection and mode decision is presented, based on a reverse bottom-to-top visiting order of CUs in the quadtree. By visiting smallest CUs first, information can be extracted to make decisions on larger CUs. The encoder adaptively selects whether a CTU is encoded using the reverse CU visiting, allowing for considerably faster encoding under all conditions. Experimental results show that the algorithm achieves on average 21% speedups over previous state-of-the-art fast HEVC algorithms, and up to 36% for some sequences, at very limited efficiency losses.

Most video coding standards, including the state-of-the-art High Efficiency Video Coding (HEVC), make use of sub-pixel Motion Estimation (ME) with Motion Vectors (MV) at fractional precisions to achieve high compression ratios. Unfortunately, sub-pixel ME comes at very high computational costs due to the interpolation step and additional motion searches. In this paper, a fast sub-pixel ME algorithm is proposed. The MV precision is adaptively selected on each block to skip the half or quarter precision steps when not needed. The algorithm bases the decision on local features, such as the behaviour of the residual error samples, and global features, such as the amount of edges in the pictures. Experimental results show that the method reduces total encoding time by up to 17.6% compared to conventional HEVC, at modest efficiency losses.

In this paper, a fast transcoding solution from H.264/AVC to HEVC bitstreams is presented. This solution is based on two main modules: a coding unit (CU) classification module that relies on a machine learning technique in order to map H.264/AVC macroblocks into HEVC CUs; and an early termination technique that is based on statistical modeling of the HEVC rate-distortion (RD) cost in order to further speed-up the transcoding. The transcoder is built around an established two-stage transcoding. In the first stage, called 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 both the CU classification model and the early termination sieves, that are used in the second stage (called the transcoding stage). The solution is tested with well-known video sequences and evaluated in terms of RD and complexity. The proposed method is 3.83 times faster, on average, than the trivial transcoder, and 1.8 times faster than a previous transcoding solution, while yielding a RD loss of 4% compared to this solution.

The new video coding standard, High Efficiency Video Coding (HEVC), was developed to succeed the current standard, H.264/AVC, as the state of the art in video compression. However, there is a lot of legacy content encoded with H.264/AVC. This paper proposes and evaluates several transcoding algorithms from the H.264/AVC to the HEVC format. In particular, a novel transcoding architecture, in which the first frames of the sequence are used to compute the parameters so that the transcoder can learn the mapping for that particular sequence, is proposed. Then, two types of mode mapping algorithms are proposed. In the first solution, a single H.264/AVC coding parameter is used to determine the outgoing HEVC partitions using dynamic thresholding. The second solution uses linear discriminant functions to map the incoming H.264/AVC coding parameters to the outgoing HEVC partitions. This paper contains experiments designed to study the impact of the number of frames used for training in the transcoder. Comparisons with existing transcoding solutions reveal that the proposed work results in lower rate-distortion loss at a competitive complexity performance.

Merge prediction is a novel technique introduced in the HEVC standard to improve inter-prediction exploiting redundancy of themotion information. We propose in this paper a new approach to enhance the Merge mode in a typical HEVC encoder using parametric transformations of the Merge prediction candidates. An Enhanced Inter-Prediction module is implemented in HEVC using Merge Prediction Transformation (MPT), integrated with the HEVC new features such as the large coding units (CU) and the recursive prediction unit partitioning. The MPT parameters are quantised according to the CU depth and the current QP. The optimal quantization steps are derived via statistical analysis as illustrated in the paper. Results show consistent improvements over conventional HEVC encoding in terms of rate-distortion performance, with a small impact on the encoding complexity and negligible impact on the decoding complexity.

The HEVC standard makes use of inter-prediction to exploit temporal redundancy in order to obtain efficient compression. A novel approach is proposed in this paper where the inter-prediction for a coding unit (CU) is further enhanced by means of simple parametric transformations. The Enhanced Inter-Prediction (EIP) module is embedded within the mode-decision module at a CU level, to obtain a more accurate inter-prediction and hence possibly reducing the residuals before transform and quantisation. The EIP parameters are encoded in the CU header, and the exact rate-distortion (RD) cost after reconstruction is computed to make sure that the EIP is only used when it is effective. The approach is also improved by means of efficient quantisation of the EIP parameters. The optimal quantisation steps for each CU are found following from analytical considerations as illustrated in the paper. Results show consistent improvements over conventional HEVC encoding in terms of PSNR and bitrate performance.

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.

Inter-prediction based on block-based motion estimation (ME) is used in most video codecs. The closer the prediction to the target block, the lower the residual, and thus more efficient compression can be achieved. In this paper, a new technique called enhanced inter-prediction (EIP) is proposed to improve the prediction candidates using an additional transformation acting while performing ME. A parametric transformation acts within the coding loop of each block to modify the prediction for each motion vector candidate. The EIP is validated in the particular case of a single-parameter shifting transformation. This paper presents an efficient algorithm to compute the best shift for each prediction candidate and a model to select the optimal prediction based on minimum cost integrating the approach with existing rate-distortion optimization techniques in the H.264/AVC video codec. Results show significant improvements with an average of 6% bit-rate reduction compared to the original H.264/AVC.

This paper proposes an efficient MPEG-2 to High Efficiency Video Coding (HEVC) video transcoder. The objective of the transcoder is to migrate the abundant MPEG-2 video content to the emerging HEVC video coding standard. The transcoder introduces a content-based machine learning solution to predict the depth of the HEVC coding units. The proposed transcoder utilizes full re-encoding to find a mapping between the incoming MPEG-2 coding information and the outgoing HEVC depths of the coding units. Once the model is built, a switch to transcoding mode occurs. Hence, the model is content based and varies from one video sequence to another. The transcoder is compared against full re-encoding using the default HEVC fast motion estimation. Using HEVC test sequences, it is shown that a speedup factor of up to 3 is achieved, while reducing the bitrate of the incoming video by around 50%. In comparison to full re-encoding, an average of 3.9% excessive bitrate is encountered with an average PSNR drop of 0.1 dB. Since this is the first work to report on MPEG-2 to HEVC video transcoding, the reported results can be used as a benchmark for future transcoding research.

The emerging video coding standard, HEVC, is currently approaching the final stage of development prior to standardization. However, the current H.264/AVC standard is very successful, and it has been widely adopted for many applications. Thus, transcoding between these codecs will be highly needed once the HEVC codec is finalised. This paper studies the performance of one of the most common techniques for heterogeneous transcoding, motion vector (MV) reuse, in a H.264/AVC to HEVC transcoder. Furthermore, it proposes a new transcoder that is capable of complexity scalability, trading off rate-distortion performance for complexity reduction. The proposed transcoder is based on a new metric to compute the similarity of the H.264/AVC MVs, which is used to decide which HEVC partitions are tested on the transcoder.

Scalable video coding (SVC) enables low complexity adaptation of compressed video, providing an efficient solution for content delivery through heterogeneous networks and to diverse displays. However, legacy video and most commercially available content capturing devices use conventional nonscalable coding, e.g., H.264/AVC. This paper proposes an efficient transcoder from H.264/AVC to a wavelet-based SVC. It aims at exploiting the advantages offered by fine granularity SVC technology when dealing with conventional coders and legacy video. The proposed transcoder was developed to cope with important functionalities of H.264/AVC, such as flexible reference frame (RF) selection. It is able to work with different coding configurations of H.264/AVC, including IPP or IBBP with multiple RFs. Moreover, many of the techniques presented in this paper are generic in the sense that they can be used for transcoding with many popular wavelet-based and hybrid-based video coding architectures. To reduce the transcoder's complexity, motion information and residual data extracted from a compressed H.264/AVC stream are exploited. Experimental results show a very good performance of the proposed transcoder in terms of decoded video quality and system complexity.

Scalable Video Coding (SVC) enables low complexity adaptation according to transmission and display requirements, providing an efficient solution for video content delivery through heterogeneous networks. However, legacy video and most commercially available content capturing devices use conventional non-scalable coding, e.g., H.264/AVC, to compress and store video streams. As a consequence and in order to fully exploit the advantages of SVC technology, efficient transcoding from conventionally coded to scalable content is urgently needed. In this paper an efficient transcoder from H.264/AVC to a wavelet-based SVC is proposed. The complexity of the transcoder is kept very low by using information extracted directly from the decoded H.264/AVC bitstream, such as motion vectors and the presence of residual data. The proposed approach has been tested with well known benchmarking sequences, showing a good performance in terms of decoded video quality and system complexity.

Scalable Video Coding (SVC) enables low complexity adaptation of the compressed video, providing an efficient solution for video content delivery through heterogeneous networks and to different displays. However, legacy video and most commercially available content capturing devices use conventional non-scalable coding, e.g., H.264/AVC. This paper proposes an efficient transcoder from H.264/AVC to a wavelet-based SVC to exploit the advantages offerend by the SVC technology. The proposed transcoder is able to cope with different coding configurations in H.264/AVC, such as IPP or IBBP with multiple reference frames. To reduce the transcoder's complexity, motion information and presence of the residual data extracted from the decoded H.264/AVC video are exploited. Experimental results show a good performance of the proposed transcoder in terms of decoded video quality and system complexity.

Wyner-Ziv (WZ) coding of video utilizes simple encoders and highly complex decoders. A transcoder from a WZ codec to a traditional codec can potentially increase the range of applications for WZ codecs. We present a transcoder scheme from the most popular WZ codec architecture to a differential pulse code modulation/discrete cosine transform codec. As a proof of concept, we implemented this transcoder using a simple pixel-domain WZ codec and the standard H.263+. The transcoder design aims at reducing complexity as a large amount of computation is saved by reusing the motion estimation, calculated at the side information generation process, and the l-frame streams. New approaches are used to generate side information and to map motion vectors for the transcoder. Results are presented to demonstrate the transcoder performance.

Wyner-Ziv (WZ) coding of video utilizes simple encoders and highly complex decoders. A transcoder from a WZ codec to a traditional codec can potentially increase the range of applications for WZ codecs. We present a transcoder scheme from the most popular WZ codec architecture to a DPCM/DCT codec. As a proof of concept, we implemented this transcoder using a simple pixel domain WZ codec and the standard H.263+. The transcoder design aims at reducing complexity, since the transcoder has to perform both WZ decoding and DPCM/DCT encoding, including motion estimation. New approaches are used to map motion vectors for such a transcoder. Results are presented to demonstrate the transcoder performance.