This study investigates the impact of network capabilities and deep learning techniques on companies' financial performance within buyer-supplier networks. It broadens the scope by incorporating network measures such as closeness and network constraint, whereas previous studies have primarily focused on suitable buyer-supplier relationships. The analysis evaluates the effects of these network measures on companies' financial performance metrics, including asset growth rate, return on assets, and more. In addition, this study explores the impact of extended networks on company performance using deep learning techniques. The results show that companies' network capabilities are positively associated with their financial performance, highlighting the critical role of network positions in driving success. Furthermore, the findings suggest that extending the network through deep learning offers significant benefits for companies.

Machine vision-based applications have witnessed widespread adoption in diverse fields. Efficiently processing and compressing the vast amounts of video data collected by machines is crucial for these applications. To address this need, the Moving Picture Experts Group (MPEG) is developing a new' video coding standard known as Video Coding for Machines (VCM), specifically optimized for video consumed by machines in vision applications. This paper proposes an advanced multi-scale feature compression (advMSFC) method with a selective learning strategy (SLS) for the Feature Compression for VCM (FCVCM). By applying the SLS, the proposed method converts multi-scale features into a single-scale feature arranged based on channel-wise importance, enabling adaptive feature channel truncation based on the QP. The truncated feature is efficiently compressed using the latest video codec, Versatile Video Coding (VVC). The proposed method outperforms the VCM feature anchor in instance segmentation, object detection, and object tracking tasks, achieving significant Bjontegaard delta-rate (BD-rate) gains. The adaptability of our method using a single trained model for various QPs shows promise for efficient feature compression in machine vision applications.

Recently, Joint Video Experts Team (JVET) has started the standardization of a new video coding named as Versatile Video Coding (VVC) with the aim of completing by 2020. In HEVC and VVC, each candidate list of Merge/AMVP (Advanced Motion Vector Prediction) is constructed from the motion information of spatial/temporal neighboring blocks to enhance the performance of inter predictive coding by utilizing available motion information. In this paper, we propose a method to derive candidate lists of Merge/AMVP with more reliable motion information having high correlation by considering the block shape of the current Coding Unit (CU). Experiment results show the performance of the proposed method in the reference SW codec of VVC named as VVC Test Model (VTM).

Medium grained scalability (MGS) of scalable video coding is expected to be of high interest due to the advantage of packet-based scalability. In this paper, we study multilayer adaptation for MGS-based bitstream. Our adaptation method not only considers which packets to be dropped, but also modifies layer dependency of NAL units to maintain bitstream conformance. Compared to conventional method of SVC, the proposed method can both expand the range of supported bitrates and improve the quality.

Performance of cross-layer protocols that recommend the relay of corrupted packets to higher layers can be improved significantly by accurately inferring/predicting the bit error rate (BER) in the packets. In practice, higher layers observe the bits only after some hard decision. Hence physical layer link-quality indications, such as the signal strength of each individual bit, are not observable at higher layers. Therefore, it is essential to identify practically observable variables, which can be used for reasonably robust channel state inference/prediction (CSI/CSP). Here, inference specifically refers to estimating the BER in an already received packet, while prediction refers to anticipating the BER in a future packet. In this paper, we note that, in practical 802.11b devices, it is possible to acquire a Signal to Silence Ratio (SSR) indication and measure the background traffic intensity (p) on a per packet basis. This paper, thus presents a measurement-based study that analyzes the utility of SSR andpas side-information for CSI/CSP. In this work, we exploit the method of types to measure the robustness of the observable side-information. Our analysis and simulations based on an extensive set of actual 802.11b traces exhibit the practical utility of the considered observable variables.

Many techniques exist for adapting videos to satisfy heterogeneous resource conditions or user preferences, whereas selection of the best adaptation operation among various choices usually is either ad hoc or inefficient. To provide a systematic solution, we present a conceptual framework based on utility function (UF), which models video entity, adaptation, resource, utility, and the relations among them. In order to support real-time video adaptation, we present a content-based statistical paradigm to facilitate the prediction of UF for real-time transcoding of live videos. Instead of modelling the UF through analytical models, as in the conventional rate-distortion framework, the proposed approach formulates the prediction as a classification and regression problem. Each video clip is classified into one of distinctive categories and then local regression is used to accurately predict the utility value. Our extensive experiment results based on MPEG-4 transcoding demonstrate that the proposed method achieves very promising performance - up to 89% accuracy in choosing the optimal transcoding operation (in both spatial and temporal dimensions) with the highest quality over a diverse range of target bit rates

The multiple reference frame motion compensation (MRMC) supported by H.264 makes use of the redundancy which is between multiple frames to enhance the coding efficiency over a scheme using the single reference frame motion compensation (SRMC) in which motion vectors are searched over a single reference frame. And, the technique using multiple reference frames can combat the channel errors efficiently. However, searching the motion vectors in multiple frames may require a huge computing time. This paper proposes a novel motion estimation procedure, which has a lower search complexity without sacrificing image quality. To reduce the complexity of motion estimation procedure, we use a temporary motion vector generated with little computation. The temporary motion vector is calculated from the motion vector map composed of motion vectors between successive frames, and used to predict the optimal motion vector for a reference frame. The proposed scheme requires the lower complexity than conventional schemes by using the temporary motion vector and refinement process over a narrow search range around the temporary predictive motion vector. Since the temporary predictive motion vector effectively chases the optimal motion vector for each reference frame, the encoded image quality by proposed scheme is very similar to that of full search algorithm. The proposed motion estimation process consists of three phases: 1)making a vector map between two consecutive frames, where the vector map is constructed by copying motion vectors which have been estimated in first reference frame, 2) composing a temporary motion vector with element vectors which are in the vector map, and 3) finally, the temporary predictive motion vector is refined over a narrow search range. We show experimental results which demonstrate the effectiveness of the proposed method. To compare the proposed motion estimation algorithm with the conventional schemes, we check the CPU times consumed by ME module in H.264 encoder using the proposed scheme. In the results, CPU time consumed by the proposed scheme has been reduced significantly without additional distortion of the encoded video quality.

In this paper, we propose a fast ME (motion estimation) algorithm for MPEG-4 to H.264 transcoder. The transcoding speed is affected dominantly by the computational complexity of encoder part in transcoder, where ME module of H.264 encoder has high complexity due to considering 7 modes. In order to increase the speed of transcoding between MPEG-4 and H.264, we use 3 PMVs (predicted motion vectors) and the mode information of MBs (macroblocks) provided from the decoder part of transcoder. Since the proposed 3 PMVs are very close to an optimal motion vector, and we consider only some restricted modes according to the MB information transferred from decoder part, the proposed scheme can speed up the transcoding procedure without loss of image quality. We show simulation results that CPU time consumed by the proposed scheme has been reduced significantly while the transcoded video quality remains unchanged.

In order to cater to the diversity of terminals and networks, efficient, and flexible adaptation of multimedia content in the delivery path to end consumers is required. To this end, it is necessary to associate the content with metadata that provides the relationship between feasible adaptation choices and various media characteristics obtained as a function of these choices. Furthermore, adaptation is driven by specification of terminal, network, user preference or rights based constraints on media characteristics that are to be satisfied by the adaptation process. Using the metadata and the constraint specification, an adaptation engine can take an appropriate decision for adaptation, efficiently and flexibly. MPEG-21 Part 7 entitled Digital Item Adaptation standardizes among other things the metadata and constraint specifications that act as interfaces to the decision-taking component of an adaptation engine. This paper presents the concepts behind these tools in the standard, shows universal methods based on pattern search to process the information in the tools to make decisions, and presents some adaptation use cases where these tools can be used.

Recently, FD (frame dropping)-CD (coefficient dropping) transcoding has been considered, mainly due to the low computational complexity and simple implementation. However, CD errors in the FD-CD transcoding scheme tend to be propagated and they have a significant effect on the qualities of decoded images. We derive the error characteristics incurred by CD operations and propose an effective estimation model that adaptively describes well the characteristics of propagation errors in the compressed domain. Furthermore, we apply the proposed model to distortion control achieving nearly constant distortion allocation among frames. Simulations show that the proposed model is quite accurate in estimating the overall distortions and is effectively applied to distortion control over a range of sequences with varying scene types.

Utility function based transcoding is an efficient systematic solution for choosing optimal media transcoding operation to meet dynamic resource constraints (such as bandwidth). However, to date the real-time generation of utility function is not feasible due to computational complexity. In this paper we present a content-based utility function prediction framework for real-time MPEG-4 video transcoding. We develop a statistical approach combining real-time compressed-domain feature extraction, content-based pattern classification and regression. Our extensive experiment results demonstrate that the proposed method achieves very promising prediction accuracy - up to 89% in choosing the optimal transcoding operation with the highest quality from multiple alternatives meeting the same target bitrate.

Many techniques exist for adapting videos to satisfy heterogeneous resource conditions or user preferences. However, selections of appropriate adaptations among various choices are often ad hoc. To provide a systematic solution, we present a general conceptual framework to model video entity, adaptation, resource, utility, and relations among them. The framework extends the conventional rate-distortion model in terms of flexibility and generality. It allows for formulations of various adaptation problems as resource-constrained utility maximization. It also facilitates new approaches to predicting critical information about resource-utility relations. We apply the framework to a practical case in dynamic bit rate adaptation. Furthermore, we present a description tool, which has been accepted as a part of the MPEG-21 digital item adaptation (DIA), to support utility-based adaptation.

In this paper, we consider a floorplanning problem in the physical design of very large scale integration. We focus on the problem of placing a set of blocks (modules) on a chip with the objective of minimizing area of the chip as well as total wire length. The blocks have different areas and their shapes are either fixed (predetermined) or flexible (to be determined). We use the sequence-pair suggested by Murata et al. (see ibid, vol.15, no.12, p.1518-1524, 1996) to represent the topology of nonslicing floorplans and present two methods to obtain a floorplan from a sequence-pair. One is a construction method, and the other is a method based on a linear programming model. The two methods are embedded in simulated annealing algorithms, which are used to find a near optimal floorplan. Results of computational experiments on the Microelectronics Center of North Carolina benchmark examples show that the proposed algorithms work better than existing algorithms.

Focuses on production scheduling in a semiconductor wafer fab producing multiple product types that have different due dates and different process flows. In the wafer fab, wafer lots are processed on serial and batch processing workstations, each of which consists of parallel identical machines. Machines in serial processing workstations process wafer lots one by one, while those in batch processing workstations process several wafer lots of the same recipe at the same time. What needs to be done for production scheduling are lot release control, lot scheduling, and batch scheduling. For these three decision problems, we develop several rules which use information such as order sizes (numbers of lots in orders) and processing status of the wafer lots. To evaluate these new rules, we use a simulation model in which the three decision problems are considered simultaneously. Simulation results show that the new rules work better than existing rules in terms of total tardiness of the orders.