Streaming 360° video is challenging, as its bandwidth exceeds that of most users. A popular solution to this problem is tiled-based streaming. Live streaming of tiled 360° video requires encoding tiles under strict time constraints. Hardware encoders, such as NVIDIA's NVENC, are used to meet this requirement. However, selecting appropriate encoding parameters for NVENC is difficult because existing benchmarks do not consider tiled-based streaming and evaluate only a subset of the available encoding parameters. In this paper, we benchmark the rate-distortion performance and the encoding speed of NVENC and recommend encoding parameters for live 360° video tiled streaming. Additionally, since we deem unfit existing 360° videos for streaming use cases, we first craft a dataset of twenty long-duration 8K/30 fps stereoscopic 360° videos, provided in both stitched and unstitched formats. Our code and data is available publicly at https://github.com/Tiled360Benchmark/360DatasetAndBenchmark.

This work introduces a novel approach for correcting a single-bit error in a CRC-protected message, which is based on a perfect hash table that can be queried by a non-zero CRC syndrome. The paper primarily focuses on presenting the new table structure and includes a comparative analysis of various CRC-based error correction methods, demonstrating that ours strikes an excellent balance between correction complexity and memory requirements. Complexity evaluation is performed in terms of the number of 2-input gate operations.

Virtual reality applications possess significant societal potential, capable of revolutionizing user experiences and generating substantial revenue. However, their high demand for bit rates poses significant challenges. The MPEG Immersive Video (MIV) standard, an integral component of MPEG-I, is designed to efficiently compress visual content from multiple cameras by pruning redundant information. This article proposes a new method to enhance the compression efficiency of MIV by grouping and preserving small clusters of non-pruned pixels that would otherwise be discarded in the default configuration of the Test Model for Immersive Video (TMIV). Experimental results demonstrate that the proposed method attains an average Bjøntegaard-Delta bitrate (BD-BR) reduction of $3.35 \%$ across six tested sequences when compared to TMIV with the default configuration. Notably, one of them exhibits a reduction reaching $5.12 \%$.

Utilizing frame-compatible (FC) formats is a common strategy for leveraging the existing single-view video transmission infrastructure to stream stereoscopic videos. However, using this method often comes with challenges, as stereoscopic video requires higher transmission bandwidth and larger memory buffers on the decoder compared to single-view videos. When it comes to stereoscopic 360∘videos, these requirements become more challenging since they ask for ultra-high-resolution formats with high frame rates (e.g., 6K, 8K, or 12K at 100 frames per second) to provide an acceptable quality of experience (QoE) to the users (4K on a 120∘viewport). To address these challenges, sub-sampled versions of the left and right views are usually used to form the spatial FC format, leading to a loss of visual quality. In this paper, we first analyze the amount of distortion due to hor-izontal and vertical downsampling in equirectangular projection (ERP). Then, we propose a novel region-dependent downsampling packing (RDDP) method using horizontal downsampling which exploits the uneven sampling characteristic of the ERP for stereoscopic 360∘videos. Experimental results, using the VVC (VVenC) encoder, show that compared with the standard side-by-side (SbS) format, the proposed RDDP method for both views provides an average of around 8.7% and 7% of Bjentegaard-Delta bitrate (BD-BR) reduction for Random Access (RA) and Low Delay B (LDB) configurations, respectively with almost the same encoding time of SbS on average.

The Affine motion estimation (AME) in Versatile Video Coding (VVC) can predict complex non-translational motions such as rotation, zoom, or shearing more effectively than the translational motion estimation (TME) tools, at the cost of greatly increased computational complexity. In this paper, to reduce encoding complexity, we propose a novel adjustable fast decision method for AME in VVC. Our method skips the AME process for blocks with low TME rate-distortion (RD) cost as we observed that skipping them reduces the encoding time without significantly affecting the compression performance. A distinctive feature of the proposed method is that it can be progressively adjusted to provide different compromises between speed-up and compression performance. First, a default TME RD cost threshold is estimated using a Multiple Linear Regression (MLR) model and then, adjusted to achieve the desired trade-off between speed-up and coding performance. Experimental results show that, with the default threshold, the proposed method can reduce the VTM encoding time by 8% on average, on classes B, C, and D, with a Bjøntegaard-Delta bitrate (BD-BR) of 0.44%. For the same classes and using 1.5 times the default threshold, it can reach 11% with a BD-BR of 0.82%.

In this paper, we propose a new deep learning-based quality ranking framework to assist video list decoding methods in the context of unreliable video transmissions. The objective is to identify an intact image (corrected video frame) among a list of candidate images generated by a list decoding method, where all candidates, except for the intact image are corrupted. The framework comprises a deep learning-based no-reference image quality assessment (NR-IQA) for non-uniform video distortions (NUD) system to rank the candidate images according to their quality, which allows identifying the best one. To show the validity of our proposed framework, we develop an NR-IQA system relying on a proven patch-based convolutional neural network (CNN) architecture, which we adapt to better account for the non-uniform distortions observed in the candidate images, e.g., H.265 transmission errors during wireless communications. Specifically, we modify the patch size on which our CNN for non-uniform distortions (CNN-NUD) operates to capture a larger and more meaningful spatial context. Moreover, we develop a new training database using images resulting from various bit modifications in the received video packets, to simulate the list decoding process, and train the system using a full reference IQA (FR-IQA) method. Experiments on intra frames of videos encoded using H.265 show the ability of this system to identify an intact image among a set of five candidate images with an average accuracy of 96.6%, whereas traditional NR-IQA metrics or the initially trained CNN system offer poor accuracy ranging between 15.7% and 33.6%, respectively.

Cloud gaming is a rising new trend for remote video gaming. Players send their commands using a thin-client device to a graphics rendering cloud server and receive a compressed video stream in response. However, video games with complex textures and motions, especially at high resolutions, require a substantial bitrate to deliver good visual quality. When the player’s Internet connection is constrained or fluctuates, the visual quality may be significantly reduced, which negatively impacts the playing experience. In this paper, we present an Action-awaRe COmbat moDEl (ARCODE) for massively multiplayer online role-playing games (MMORPGs) running on cloud gaming platforms to improve compression efficiency. ARCODE captures different action data for different object types in the battle scene and determines the importance of each object relative to the player in each game state, considering the actions at the time. Based on the significance of each object to the player, the model determines how frequently its position should be updated. Reducing the number of motion updates in the scene leads to fewer bits needed to encode the video frames. Our experimental results on various test cases show that, for similar visual quality as that of the traditional approach, ARCODE can reduce the video bitrate from 9% to over 40%.

This paper analyzes the benefits of extending CRC-based error correction (CRC-EC) to handle more errors in the context of error-prone wireless networks. In the literature, CRC-EC has been used to correct up to 3 binary errors per packet. We first present a theoretical analysis of the CRC-EC candidate list while increasing the number of errors considered. We then analyze the candidate list reduction resulting from subsequent checksum validation and video decoding steps. Simulations conducted on two wireless networks show that the network considered has a huge impact on CRC-EC performance. Over a Bluetooth low energy (BLE) channel with Eb/No=8 dB, an average PSNR improvement of 4.4 dB on videos is achieved when CRC-EC corrects up to 5, rather than 3 errors per packet.

In this paper, we introduce a novel cyclic redundancy check (CRC)-based single error correction method which we apply to robust H.264 Baseline video decoding. Unlike state-of-the-art methods, the proposed correction algorithm does not require lookup tables as it determines the error location based on binary operations using the computed link layer CRC syndrome. Since multiple errors can lead to the same CRC syndrome as a single error, verification of the corrected packet is performed through a non-desynchronizing bits validation (NDBV), which forwards only compliant packets to the video decoder. Simulations on the H.264 Baseline profile show an average gain of 3.04 dB and 2.36 dB over state-of-the-art spatio-temporal error concealment (STBMA) and NDBV + STBMA reconstruction methods, respectively, at a residual bit error rate of $10^{-6}$.

Virtual reality (VR) streaming is impaired by the large amount of data required to deliver 360-degree video resulting in low-quality end user experience when network bandwidth is limited, or latency is high. To address these challenges, proposed in this paper is a novel method for viewpoint prediction for long-term horizons in VR streaming. This method uses a long short-term memory (LSTM) encoder-decoder network to carry out a sequence-to-sequence prediction. To enhance the results obtained by this network, experiments are performed using viewpoint information from users on low-latency networks. By applying an effective tile-based quality assignment after viewpoint prediction, a 61% average bandwidth reduction, with respect to the transmission of the whole ERP frame, is achieved along with a high-quality viewport rendered to the end user.

This paper presents an efficient method for encoding common projection formats in 360° video coding, in which we exploit inactive regions. These regions are ignored in the reconstruction of the equirectangular format or the viewport in virtual reality applications. As the content of these pixels is irrelevant, we neglect the corresponding pixel values in rate-distortion optimization, residual transformation, as well as in-loop filtering and achieve bitrate savings of up to 10%.

Recovering of the depth structure of a scene from monocular video content provides an important advantage in applications such as AR (placing and removing of objects) or 3D-TV and 3D cinema (2D-to-3D video conversion). In this paper, we present an automatic method to generate relative depth maps from monocular video sequences. It relies on the dynamic occlusion depth cue to recover the depth order of objects in the scene. The forward and backward motion analysis between each two consecutive frames allows the calculation of their dynamic occlusions. We estimate the motion using a modified version of the EpicFlow. Our modifications to this optical flow method made it coherent in forward-backward directions without compromising its performance. Thanks to this new feature, occlusions are simpler to calculate than the approaches used in the relevant literature. The obtained occlusions allow order deduction of the objects contained in the image. These objects are obtained using a segmentation approach which considers both color and motion. Ours results show a small improvement to the quality of the optical flow while adding the forward/backward coherence. With respect to the depth ordering our approach obtains slightly better results than the reference method while removing a computationally costly step from the processing.

In this paper, we propose a fast H.264-to-HEVC transcoder composed of a motion propagation algorithm and a fast mode decision framework. The motion propagation algorithm creates a motion vector candidate list at the coding tree unit (CTU) level and, thereafter, selects the best candidate at the prediction unit level. This method eliminates computational redundancy by pre-computing the prediction error of each candidate at the CTU level and reusing the information for various partition sizes. The fast mode decision framework is based on a post-order traversal of the CTU and includes several mode reduction techniques. In particular, the framework permits the early termination of the rate distortion cost computation, a highly complex task, when a mode is unpromising. Moreover, a novel method exploits the data created by the motion propagation algorithm to determine whether a coding unit must be split. This allows the pruning of unpromising sub-partitions. Compared with a cascaded pixel-domain transcoding approach, the experimental results show that the proposed solution using one reference frame is on average 8.5 times faster, for an average Bjøntegaard delta-rate (BD-Rate) of 2.63%. For a configuration with four reference frames, the average speed-up is 11.77 times and the average BD-Rate is 3.82%.

The latest video coding standards, H.264 and H.265, are highly vulnerable in error-prone networks. Reconstructed packets may exhibit significant degradation in terms of peak signal-to-noise ratio and visual quality. This paper presents a novel list-decoding approach exploiting the receiver side user datagram protocol (UDP) checksum. The proposed method identifies the possible locations of errors by analyzing the pattern of the calculated UDP checksum. This permits considerably reducing the number of candidate bitstreams in comparison to conventional list decoding approaches. When a packet composed of N bits contains a single-bit error, instead of considering N candidate bitstreams, as is the case in conventional list decoding approaches, the proposed approach considers N/32 candidate bitstreams, leading to a reduction of 97% of the number of candidates. For a two-bit error, the reduction increases to 99.6%. The method's performance is evaluated using H.264 and H.265 test model software. Our simulation results reveal that, on average, the error was corrected perfectly 80%-90% of the time (the original bitstream was recovered). In addition, the proposed approach provides, on average, a 2.79-dB gain over frame copy (FC) error concealment using the joint model and a 3.57-dB gain over our implementation of FC error concealment in the High Efficiency Video Coding test model.

Rate-constrained motion estimation (RCME) is the most computationally intensive task of H.265/HEVC encoding. Massively parallel architectures, such as graphics processing units (GPUs), used in combination with a multi-core central processing unit (CPU), provide a promising computing platform to achieve fast encoding. However, the dependencies in deriving motion vector predictors (MVPs) prevent the parallelization of prediction units (PUs) processing at a frame level. Moreover, the conditional execution structure of typical fast search algorithms is not suitable for GPUs designed for data-intensive parallel problems. In this paper, we propose a novel highly parallel RCME method based on multiple temporal motion vector (MV) predictors and a new fast nested diamond search (NDS) algorithm well-suited for a GPU. The proposed framework provides fine-grained encoding parallelism. Experimental results show that our approach provides reduced GPU load with better BD-Rate compared to prior full search parallel methods based on a single MV predictor.

Rate-constrained motion estimation (RCME) is considered to be the most time-consuming process of H.265/HEVC encoding. Massively parallel architectures, such as graphics processing units (GPUs), used in combination with a multi-core central processing unit (CPU), provide a promising computing platform to achieve fast encoding. However, the inherent dependencies in the process for deriving motion vector predictors (MVPs) prevent the parallelization of prediction units (PUs) processing. In this paper, we present a framework for performing a two-stage parallel RCME, in which the RCME of all the PUs of a frame can be calculated in parallel. A novel method is introduced to overcome the dependencies inherent to the derivation of MVPs. Multiple temporal predictors (MTPs) within the two-stage parallel RCME framework provide fine-grained parallelism encoding without significant BD-Rate penalty, compared to serial encoding. Experimental results show that our proposed approach achieves a BD-Rate improvement of over 1% as compared to state-of-the-art parallel methods providing similar time reductions.

In mobile web conferencing, slide decks should be optimized before delivery to meet the constraints and environments of target mobile devices. To deliver optimally adapted slides, a trade-off between the visual aspect and delivery time must be reached. Static adaptation methods are CPU-intensive, and require large storage space. The dynamic approach is attractive as the optimal version is created on the fly when the actual slide is to be shared. Existing dynamic solutions are optimized for the resolution of the target mobile device and use good visual quality settings. However, they do not control the resulting data size, which creates serious usability issues, such as increasing the delivery time. Prediction-based methods require much less memory and processing resources than static approaches while yielding an excellent user experience. In this paper, we propose a multi-objective dynamic content adaptation framework, in which we maximize the visual quality and minimize the delivery time simultaneously. We compare our solution with an ideal optimal point, called utopia, and with all the optimal solutions (Pareto front) provided by a static exhaustive system. The obtained results show that our framework yields solutions very close to the utopia and, for the majority of the documents tested, the obtained solutions are on the Pareto front.

In the context of motion estimation (ME) for video coding, the rate-constrained successive elimination algorithm (RC-SEA) safely eliminates candidate motion vectors while preserving the optimal candidate chosen by the block matching algorithm (BMA). This paper describes a technique for reusing ME information from rectangular to square prediction units in order to reduce the search area without altering the optimal candidate chosen by the BMA. Our experiments show that, on average, when this optimization is combined with the RCSEA in the HEVC HM encoder reference software, the number of sum of the absolute differences (SAD) operations drops by 94.9%, resulting in a speedup of 6.13x in full search mode. Although identical coding decisions cannot be guaranteed when multiple optimal solutions exist, the average impact on BD-PSNR is 0.0002 dB.

High efficiency video coding (HEVC) doubles the compression ratio as compared to H.264/AVC, for the same quality. To achieve this improved coding performance, HEVC presents a new content-adaptive approach to split a frame into coding units (CUs), along with an increased number of prediction modes, which results in significant computational complexity. To lower this complexity with intra coding, in this paper, we develop a new method based on global and directional gradients to terminate the CU splitting procedure early and prevent processing of unnecessary depths. The global and directional gradients determine if the unit is predicted with high accuracy at the current level, and where that's the case, the CU is deemed to be non-split. Experimental results show that the proposed method reduces the encoding time by 52% on average, with a small quality loss of 0.07 dB (BD-PSNR) for all-intra scenarios, as compared to the HEVC reference implementation, HM 15.0.

The recent High Efficiency Video Coding (HEVC) standard was designed to achieve significantly improved compression performance compared to the widely used H.264/AVC standard. This achievement was motivated by the ever-increasing popularity of high-definition video applications and the emergence of ultra-HD. Unfortunately, this comes at the expense of a significant increase in computational complexity for both inter and intra coding. To alleviate this problem, in this paper, we propose a fast intra mode decision method based on improved edge detection, consideration of most relevant modes from neighboring blocks, and classification of SATD costs permitting the elimination of several candidate modes prior to rate distortion optimization (RDO). Experimental results show that the proposed method provides time reduction up to 39.2% and an average 35.6% with negligible quality loss as compared to the HEVC reference implementation HM 15.0.

In this paper, we propose a fast mode decision framework and a fast motion estimation algorithm for H.264 to High Efficiency Video Coding (HEVC) transcoding. The fast mode decision framework employs a post-order (bottom-up) traversal of the coding tree unit (CTU) quadtree. Based on this traversal and H.264 information, several strategies are proposed to reduce HEVC modes to be tested and a rate-distortion (RD) cost prediction model is used to terminate the processing of a tested mode early. The proposed fast motion estimation algorithm selects the best candidate from a list of H.264 motion vectors (MVs) and previously encoded HEVC MVs. Compared to a full re-encoding, experimental results show that the proposed solution achieves speed-ups of up to 12.75x, for an average BD-Rate of 3.28%.

This paper proposes a solution for the problem of unnecessary cost function evaluations, found when combining the successive elimination algorithm with a spiral scan search ordering. Our experiments show that the implementation of such a combination inside the HEVC reference software leads to unnecessary cost function evaluations. On the tested video sequences, an average of 3.46% unnecessary cost function evaluations was measured. Considering only small block sizes (e.g., 4×8 and 8×4), this average rises to 8.06%. To solve this problem, we propose an adaptive scan ordering of block matching candidates within the search area. When used with our early termination threshold, the proposed approach will only evaluate necessary cost functions, without impacting rate-distortion.

Error concealment has long been identified as the last line of defense against transmission errors. Since error handling is outside the scope of video coding standards, decoders may choose to simply ignore corrupted packets or attempt to decode their content. In this paper, we present a novel joint source-channel decoding approach that can be applied to received video packets containing transmission errors. Soft-output information is combined with our novel syntax-element-level maximum likelihood decoding framework to effectively extract valid macroblocks from corrupted H.264 slices. Simulation results show that our video error correction strategy provides an average peak signal-to-noise ratio (PSNR) improvement near 2 dB compared to the error concealment approach used by the H.264 reference software, as well as an average PSNR improvement of 0.8 dB compared to state-of-the-art error concealment. The proposed method is also applicable when only hard-information is available, in which case it performs better than state-of-the-art error concealment especially in high error conditions. Finally, in our simulations, the proposed method increased the decoder computational complexity by only 5% to 20%, making it applicable for real-time applications.

In this paper, we propose a new class of search ordering algorithms to reduce the computational cost of motion estimation in video coding. We show that conventional search orderings, such as spiral search, can weaken the filtering criterion of rate-constrained successive elimination algorithms. Based on this new insight, we derive a new search ordering that takes into account the impact of the rate constraint. Our simulation results demonstrate that, on average, the amount of SAD operations required to encode the tested sequences, is reduced by 2.86%, when compared to the H.264 JM reference software's implementation of spiral search. For sequences with unpredictable motion, this reduction is greater than 5% and can exceed 10% when smaller block partitions are evaluated.

In the context of mobile Web conferencing, slide documents are generally transcoded into JPEG format and wrapped into a Web page prior to delivery. Given the diversity of these devices and their networks, dynamically identifying the optimal transcoding parameters is very challenging, as the number of transcoding parameters combinations could be very high. Current solutions use the resolution of the target mobile device and a fixed quality factor as transcoding parameters. However, this technique allows no control over the resulting file size, which, if too large, might increase the delivery time and negatively affect users' experience. Another solution (content selection) which leads to better quality consists in creating several versions and, at delivery time, selecting the best one. However, such a solution is computationally expensive. In this paper, we propose a prediction-based framework which computes near-optimal transcoding parameters dynamically with far less computations. We propose five methods based on this framework. The first predicts near-optimal transcoding parameters, while the others improve their accuracy. From the set of documents tested, two of the proposed methods reach optimality 14% and 30% of the time, respectively. Moreover, the average deviation from optimality for the proposed methods varies from 6% to 3%, with a complexity varying from 1 to 5 transcoding operations.

Real time video applications need to handle transmission errors, as retransmissions are impractical. In this paper, we present a novel joint source channel decoding approach for video error correction. Soft-output information is combined with our syntax element-level maximum likelihood framework to effectively extract valid macroblocks from corrupted H.264 slices. Simulation results show that our video error correction strategy provides better visual quality by reducing the size of the concealment region. Our observations indicate an average PSNR improvement of 0.8 dB (QP=36), with peaks above 10 dB, over state-of-the-art error concealment at a bit error rate of 10-3.

In this paper, we address the problem of adapting video files to meet terminal file size and resolution constraints while maximizing visual quality. First, two new quality estimation models are proposed, which predict quality as function of resolution, quantization step size, and frame rate parameters. The first model is generic and the second takes video motion into account. Then, we propose a video file size estimation model. Simulation results show a Pearson correlation coefficient (PCC) of 0.956 between the mean opinion score and our generic quality model (0.959 for the motion-conscious model). We obtain a PCC of 0.98 between actual and estimated file sizes. Using these models, we estimate the combination of parameters that yields the best video quality while meeting the target terminal's constraints. We obtain an average quality difference of 4.39% (generic model) and of 3.22% (motion-conscious model) when compared with the best theoretical transcoding possible. The proposed models can be applied to video transcoding for the Multimedia Messaging Service and for video on demand services such as YouTube and Netflix.

Today, professional documents, created in applications such as PowerPoint and Word, can be shared using ubiquitous mobile terminals connected to the Internet. GoogleDocs and EasyMeet are good examples of such collaborative web applications dedicated to professional documents. The static adaptation of professional documents has been studied extensively. Dynamic adaptation can be very useful and practical for interactive multimedia applications, because it allows the delivery of highly customized content to the end user without the need to generate and store multiple transcoded versions. In this paper, we propose a dynamic framework that enables us to estimate transcoding parameters on the fly to generate near-optimal adapted content for each user. The framework is compared to current dynamic methods as well as to static adaptation solutions. We show that the proposed framework provides a better tradeoff between quality and storage compared to other static and dynamic approaches. To quantify the quality of the adapted content, we introduce a measure of the quality of the experience based on the visual quality of the adapted content, as well as on the impact of its total delivery time. The framework has been tested on (but is not limited to) OpenOffice Impress presentations.

In mobile video applications, where unreliable networks are commonplace, corrupted video packets can have a profound impact on the quality of the user experience. In this paper, we show that, in a wide range of operating conditions, selectively reusing data resulting from decodable errorneous packets leads to better results than frame copy. This selection is guided by a novel concept that combines motion estimation and a measure of blocking artifacts at block edges to predict visual degradation caused by the decoding of erroneous packets. Simulation results show that, by using the proposed solution, the H.264/AVC JM reference software decoder can select the best option between frame copy and the erroneous frame decoding in 82% of test cases. We also obtain an average gain of 1.95 dB for concealed frames (when they differ from those concealed by the JM decoder).

The problem of adapting JPEG images to satisfy constraints such as file size and resolution arises in a number of applications, from universal media access to multimedia messaging services. Visually optimized adaptation, however, commands a non-negligible computational cost which we aim to minimize using predictors. In previous works, we presented predictors and systems to achieve low-cost near-optimal adaptation of JPEG images. In this work, we propose a new approach to file size and quality prediction resulting from the Transcoding of a JPEG image subject to changes in quality factor and resolution. We show that the new predictor significantly outperforms the previously proposed solutions in accuracy.

The visual information fidelity (VIF) index is an objective quality metric that gives very accurate image similarity scores, but at the cost of very high computational complexity. In this paper, a method is presented for calculating VIF in the discrete wavelet domain using the Haar wavelet. The proposed method exploits scalar Gaussian Scale Mixture (GSM) instead of vector GSM for calculating the prediction scores. The complexity of the proposed method is assessed for five different popular image sizes and compared to other methods based on a C/C++ implementation of the algorithms. Experimental results show that the proposed method can compute the visual quality score with less than 30% of the computational complexity of the well-known SSIM index, with greater accuracy than that achieved by the original VIF index method (at about 5% of its computational complexity).

A common transcoding operation consists of reducing the file size of a JPEG image to meet bandwidth or device constraints. This can be achieved by reducing its quality factor (QF) or reducing its resolution, or both. In this paper, using the Structural SIMilarity (SSIM) index as the quality metric, we present a system capable of estimating the QF and scaling parameters to achieve optimal quality while meeting a device's constraints. We then propose a novel low-complexity JPEG transcoding system which delivers near-optimal quality. The system is capable of predicting the best combination of QF and scaling parameters for a wide range of device constraints and viewing conditions. Although its computational complexity is an order of magnitude smaller than the system providing optimal quality, the proposed system yields quality results very similar to those of the optimal system.

Reducing the file size of a JPEG image to meet bandwidth or terminal constraints is a common transcoding operation. The reduction can be achieved by reducing either the quality factor (QF) or the resolution, or both. In this paper, we analyze the impact of QF and scaling parameter choices on the quality of the resulting images, as measured by a quality metric such as the structural similarity index (SSIM). We propose a quality-aware transcoding system which considers the quality of transcoded images when QF and scaling are selected jointly. Its goal is to select QF and scaling parameters that maximize the user experience under a given viewing condition, as measured by the chosen quality metric.

The structural similarity (SSIM) index is an objective metric that gives relatively accurate similarity prediction scores with reasonable complexity. In this paper, an excellent trade-off between accuracy and complexity is presented in the form of a wavelet structural similarity index (WSSI), which is more accurate and less complex than the spatial SSIM index. Like the spatial SSIM index, the WSSI has the feature of boundedness. It computes an edge structural similarity map and an approximation structural similarity map to obtain the final similarity score. A contrast map is introduced in the wavelet domain for pooling structural similarity maps. Experimental results show that the low-complexity WSSI gives a correlation coefficient of 0.9548 between objective and subjective scores, and competes with visual information fidelity (VIF) performance.

This work presents two new algorithms to predict the file size of a JPEG image subject to transformations consisting of simultaneous changes in resolution (scaling) and in quality factor (QF). To be computationally efficient, the prediction is based solely on easily accessible image parameters such as the quality factor and the original file size. A large image corpus (100,000 images), gathered by a crawler, is divided into a training set used to optimize the predictors and into a test set used to validate the predictors. For both algorithms the prediction error is shown to be of a few percents when the output parameters are close to those of the original image while remaining reasonably attractive elsewhere. Both algorithms are simple to implement and require very little processing for the prediction itself; making them good choices for implementation in transcoding servers. Following is an example of a prediction matrix from the first algorithm for images with original quality factor (QFin) of 80 and for various scalings and output quality factors (QFout).

This article addresses the problem of designing two-channel near-perfect-reconstruction filter banks over multidimensional lattices. First, a cosine-modulated filter structure having arbitrary spatial shift and phase parameters is considered. The use of this structure leads to many possible two-channel multirate systems. The perfect reconstruction conditions are studied and appropriate choices for the parameters of the cosine-modulated structure are obtained. A simple but efficient unconstrained procedure for designing (possibly linear phase) near-perfect-reconstruction filter banks having good frequency responses (with arbitrary shapes) is proposed. A 2D design example is presented. The filter banks obtained can be used in transmultiplexers as well as in subband coders.

This paper presents some applications to FIR filter design of multi-D windows over arbitrary lattices and with arbitrary center of spatial symmetry. First, classic windows (such as Hamming, Blackman, etc.) are extended to windows over 1D and multi-D lattices with arbitrary spatial symmetry centers (which multirate applications sometimes require). Then the problem of obtaining a target frequency response with a good transition band from an ideal frequency response (made of a passband having constant gain and a stopband for which the gain is zero) is studied. A method to obtain a described target response using multi-D windows with application to the design of FIR filters is presented. Finally, a procedure for designing multi-D FIR filters by windowing is explained.

This research addresses the frequency multiplexing of multidimensional signals, having different bandwidths or defined on different lattices, with perfect or near perfect reconstruction i.e. zero or low crosstalk between signals and zero or low distortion of individual signals. The paper discusses important issues such as: what are the valid modulating frequencies, how to manage quadrature modulation, what are the conditions for perfect reconstruction, how many signals can be transmitted perfectly, when and why linear periodically time-varying (LPTV) filters must be used in the system, structure of filters to preserve compatibility with conventional frequency multiplexers and design procedures.