Abstract: The present invention relates to an inter-layer prediction method and to an apparatus implementing the method. The method may comprise the steps of generating a first block constituted by the value obtained by up-sampling the reconstruction value of a reference block of a reference layer corresponding to the current block; generating a second block constituted by a prediction value derived from intra-prediction mode of the current block; and generating a prediction block of the current block by combining sample values of the first block and the second block. Thus, intra-prediction on the current layer can be performed using the intra-prediction mode information of another layer.

Abstract: Techniques for allocating individually executable portions of executable code for execution in an Elastic computing environment are disclosed. In an Elastic computing environment, scalable and dynamic external computing resources can be used in order to effectively extend the computing capabilities beyond that which can be provided by internal computing resources of a computing system or environment. Machine learning can be used to automatically determine whether to allocate each individual portion of executable code (e.g., a Weblet) for execution to either internal computing resources of a computing system (e.g., a computing device) or external resources of an dynamically scalable computing resource (e.g., a Cloud). By way of example, status and preference data can be used to train a supervised learning mechanism to allow a computing device to automatically allocate executable code to internal and external computing resources of an Elastic computing environment.

Abstract: The present invention relates to an inter-layer prediction method and to an apparatus implementing the method. The method may comprise the steps of generating a first block constituted by the value obtained by up-sampling the reconstruction value of a reference block of a reference layer corresponding to the current block; generating a second block constituted by a prediction value derived from intra-prediction mode of the current block; and generating a prediction block of the current block by combining sample values of the first block and the second block. Thus, intra-prediction on the current layer can be performed using the intra-prediction mode information of another layer.

Abstract: The present invention relates to a video encoding method, to a video decoding method, and to an apparatus using same. The video encoding method according to the present invention comprises the steps of: setting a tile and a slice for the inputted current picture; performing encoding based on the tile and the slice; and a step of transmitting the encoded video information. The current picture may include one or more tiles and one or more slices. The largest coding units (hereinafter, referred to as LCUs) in the slice may be arranged based on a tile scan.

Abstract: Disclosed are a method and apparatus for encoding/decoding images. The image-decoding method comprises the steps of: receiving a bit stream including information regarding an NAL unit type; and checking whether or not the NAL unit in the bit stream is a reference picture based on said information regarding an NAL unit type and decoding the NAL unit. The information regarding an NAL unit type indicates whether the NAL unit is a reference reading picture or not a reference reading picture.

Abstract: In accordance with one aspect of the invention, web workers and local storages can be extended to a cloud-based environment. This allows web workers to be executed on any of a number of different cloud platforms located in a cloud, leveraging available resources to provide a quicker and more efficient processing environment for the various web workers. The present invention also provides these functionalities in a way that is transparent to not just the user, but also to the web page developer as well, eliminating the need for the web page developer to be aware of the cloud-based environment and design the web page for use therewith.

Abstract: The enhanced 3D audio/video processing apparatus according to one embodiment of the present invention may comprise: a three-dimensional (3D) content generating unit for generating 3D content including video content and analog content; a depth information generating unit for generating depth information for the video frames constituting the video content; and a signal generating unit for generating a 3D enhanced signal including the generated 3D content and the depth information.

Abstract: The present invention relates to an image decoding method and to an apparatus using same. The image decoding method includes the steps of: receiving a bitstream including random access image information; and predicting the random access image and an image following the random access image in terms of a decoding sequence on the basis of the random access image information.

Abstract: The present invention relates to a method for storing image data, and a method for parsing image data, and an apparatus for using the same, which method includes the steps of encoding the image data, and storing an NAL (Network Abstraction Layer) unit containing the encoded image data in the decoder set register in which NAL units may be stored according to the type of NAL unit.

Abstract: The present invention includes an image information decoding method which comprises: a step of receiving a bitstream that includes a network abstraction layer (NAL) unit including information related to an encoded image; and a step of parsing an NAL unit header of the NAL unit. The NAL unit header includes layer information including reserved_one_5bits for identifying an extended layer in an extended bitstream and temporal_id for identifying a temporal layer of a bitstream. The reserved_one_5bits of the layer information is received prior to the temporal_id of the layer information. Thus, a method for describing scalability information in a hierarchical bitstream is provided.

Abstract: The present invention relates to a method for storing video information, to a method for parsing video information and to an apparatus using the same. The method for storing video information according to the present invention comprises: a step of encoding video information; and a step of storing a network abstraction layer (NAL) unit including the encoded video information in a decoder configuration record. The decoder configuration record may include information indicating the type of NAL unit.

Abstract: The present invention relates to a video encoding method, to a video decoding method and to an apparatus using same. A video encoding method according to the present invention comprises: a step of encoding substreams which are rows of largest coding units (LCUs) in parallel with each other; and a step of transmitting a bit stream including the encoded substreams. The number of the substreams may be the same as the number of entry points.

Abstract: An apparatus for controlling depth/distance of sound and method thereof are disclosed, by which an audio signal can be outputted to correspond to a depth of an image, i.e., a disparity in displaying a stereoscopic image. The present invention includes extracting at least one object from an image, measuring a depth change value in accordance with a motion of the object within the image, and changing a depth/distance level of the sound based on the depth change value of the object.

Abstract: The present invention relates to an inter-layer prediction method and to an apparatus implementing the method. The method may comprise the steps of generating a first block constituted by the value obtained by up-sampling the reconstruction value of a reference block of a reference layer corresponding to the current block; generating a second block constituted by a prediction value derived from intra-prediction mode of the current block; and generating a prediction block of the current block by combining sample values of the first block and the second block. Thus, intra-prediction on the current layer can be performed using the intra-prediction mode information of another layer.

Abstract: Techniques for assessing the cost of allocation of execution and affecting the allocation of execution are disclosed. The cost of allocation of execution between a first computing device (e.g., mobile device) and one or more computing resource providers (e.g., Clouds) can be determined during runtime of the code. A computing system can operate independently of the first computing device and a computing resource provider and provide execution allocation cost assessment. Execution allocation cost can be assessed based on execution allocation data pertaining to the first computing device and computing resource providers. Power consumption of a mobile device can be used as a factor in determining how to allocate individual components of an application program between a mobile phone and a Cloud. In an Elastic computing environment, external computing resources can be used to extend the computing capabilities beyond that which can be provided by internal computing resources.

Abstract: Techniques for allocating individually executable portions of executable code for execution in an Elastic computing environment are disclosed. In an Elastic computing environment, scalable and dynamic external computing resources can be used in order to effectively extend the computing capabilities beyond that which can be provided by internal computing resources of a computing system or environment. Machine learning can be used to automatically determine whether to allocate each individual portion of executable code (e.g., a Weblet) for execution to either internal computing resources of a computing system (e.g., a computing device) or external resources of an dynamically scalable computing resource (e.g., a Cloud). By way of example, status and preference data can be used to train a supervised learning mechanism to allow a computing device to automatically allocate executable code to internal and external computing resources of an Elastic computing environment.

Abstract: Access permission can be assigned to a particular individually executable portion of computer executable code (“component-specific access permission”) and enforced in connection with accessing the services of a service provider by the individually executable portion (or component). It should be noted that least one of the individually executable portions can request the services when executed by a dynamically scalable computing resource provider. In addition, general and component-specific access permissions respectively associated with executable computer code as a whole or one of it specific portions (or components) can be cancelled or rendered inoperable in response to an explicit request for cancelation.

Abstract: Techniques for allocating individually executable portions of executable code for execution in an Elastic computing environment are disclosed. In an Elastic computing environment, scalable and dynamic external computing resources can be used in order to effectively extend the computing capabilities beyond that which can be provided by internal computing resources of a computing system or environment. Machine learning can be used to automatically determine whether to allocate each individual portion of executable code (e.g., a Weblet) for execution to either internal computing resources of a computing system (e.g., a computing device) or external resources of an dynamically scalable computing resource (e.g., a Cloud). By way of example, status and preference data can be used to train a supervised learning mechanism to allow a computing device to automatically allocate executable code to internal and external computing resources of an Elastic computing environment.

Abstract: Techniques for determining an interest in an object of interest in a given situation are disclosed. Multiple situation-based interest rating components can be provided for various situations. Each situation-based interest rating component can include an interest value indicative of interest in an object of interest in one of the situations. An input situation can then be obtained. One of the situation-based interest rating components can be identified matching an input situation. The relevance of one or more of the other situation-based interest rating components to the identified matching component can then be determined. This can, for example, be done by computing one or more distances between only the respective situation-based portions of the matching situation-based interest rating component and one or more of the other components, or based on the interest value-based portion of each component, or both.

Abstract: In a first embodiment of the present invention, a method for automated creation of a mashup is provided, the method comprising: receiving data needs of a user; identifying sources of data to satisfy the data needs by comparing the data needs to available data sources; retrieving metadata relating to the identified sources of data from a source metadata store; identifying services to satisfy the data needs by comparing the retrieved metadata to available services; retrieving metadata related to the identified services from a service metadata store; and generating a plan for supplying data from the identified sources of data to the identified services based on the retrieved metadata from the source metadata source and the retrieved metadata from the service metadata source.