Abstract: The invention discloses a pure end-to-end deep reinforcement learning for training car racing game AI bot that uses only the velocity information extracted from screen for both training and testing phases without using any internal state from game environment, such as the car facing angle. The learned AI bot can play better than the average performance of human players. In addition, the reward function is designed to consist only the velocity value, and use Ape-X distributed training framework combined with a variant of Deep Q Network to solve the sparse training signal problem caused by the reward function of an original design. Moreover, limit learner rate method is designed that improves the training efficiency and training performance. The AI bot trained in this way can achieve performance beyond the average human level and reach a level close to professional players.

Abstract: A method for enhancing quality of media uses an AI enhancing model built-in the client device to enhance the quality of video streams. The AI enhance module is pre-trained by using a neural network in the server to analyze differences between the decoded images and the raw images that are generated by the server. Wherein, the AI enhance module enhances decoded images by using algorithms which are defined by analyzing differences between the decoded images and the raw images. Such that, the enhanced images are visually more similar to the raw images than the decoded images do.

Abstract: A method for enhancing quality of media uses an artificial intelligence (AI) enhancing model built-in the client device to enhance the quality of video streams. The AI enhance module is pre-trained by using a neural network in the server to analyze differences between the decoded images and the raw images that are generated by the server. Wherein, the AI enhance module enhances decoded images by using algorithms which are defined by analyzing differences between the decoded images and the raw images. Such that, the enhanced images are visually more similar to the raw images than the decoded images do.

Abstract: A method for enhancing quality of media uses an AI enhancing model built-in the client device to enhance the quality of video streams. The AI enhance module is pre-trained by using a neural network in the server to analyze differences between the decoded images and the raw images that are generated by the server. Wherein, the AI enhance module enhances decoded images by using to algorithms which are defined by analyzing differences between the decoded images and the raw images. Such that, the enhanced images are visually more similar to the raw images than the decoded images do.

Abstract: A method for enhancing quality of media transmitted via network uses an AI enhancing model built-in the client device to enhance the quality of video streams received from network. The AI enhance module is pre-trained by using a neural network in the server to analyze differences between the decoded images and the raw images that are generated by the server. Wherein, the AI enhance module enhances decoded images by using algorithms which are defined by analyzing differences between the decoded images and the raw images that are generated by the server. Such that, the enhanced images are visually more similar to the raw images than the decoded images do.

Abstract: A method for enhancing quality of media transmitted via network uses an AI enhancing model built-in the client device to enhance the quality of video streams received from network. The AI enhance module is pre-trained by using a neural network in the server to analyze differences between the decoded images and the raw images that are generated by the server. Wherein, the AI enhance module enhances decoded images by using algorithms which are defined by analyzing differences between the decoded images and the raw images that are generated by the server. Such that, the enhanced images are visually more similar to the raw images than the decoded images do.

Abstract: A method for enhancing quality of media transmitted via network uses an AI enhancing model built-in the client device to enhance the quality of video streams received from network. The AI enhance module is pre-trained by using a neural network in the server to analyze differences between the decoded images and the raw images that are generated by the server. Wherein, the AI enhance module enhances decoded images by using algorithms which are defined by analyzing differences between the decoded images and the raw images that are generated by the server. Such that, the enhanced images are visually more similar to the raw images than the decoded images do.

Abstract: A system and a corresponding method of leveraging GPU resources to increase performance of the interact-able content browsing (ICB) service are to utilize the computation capacity of the GPU of the graphics card to share the computation load originally taken by the CPU. The raw images depicted by the browser application program executed in the server can be directly retrieved from the VRAM of the graphics card by the encoder inside the GPU to be further encoded and streamed before being forwarded directly to distant clients. Thereby, work load and bus bandwidth occupation on the CPU can be greatly reduced, the number of clients that can be served simultaneously by a single server can be increased, and the related power consumption can be substantially reduced.

Abstract: Methods, apparatuses, and computer program products for implementing interactive applications by storing and retrieving streaming video clips and associated metadata are described.

Abstract: Methods, apparatuses, and computer program products for implementing interactive applications by storing and retrieving streaming video clips and associated metadata are described.

Abstract: Methods, apparatuses, and computer program products for implementing interactive applications by storing and retrieving streaming video clips and associated metadata are described.

Abstract: A system and a corresponding method of leveraging GPU resources to increase performance of the interact-able content browsing (ICB) service are to utilize the computation capacity of the GPU of the graphics card to share the computation load originally taken by the CPU. The raw images depicted by the browser application program executed in the server can be directly retrieved from the VRAM of the graphics card by the encoder inside the GPU to be further encoded and streamed before being forwarded directly to distant clients.

Abstract: A system and a corresponding method of leveraging GPU resources to enhance performance of the interact-able content browsing (ICB) service are to utilize the computation capacity of the GPU of the graphics card to share the computation load originally taken by the CPU. The raw images depicted by the browser application program executed in the server can be directly retrieved from the VRAM of the graphics card by the encoder inside the GPU to be further encoded and streamed before being forwarded directly to distant clients. Thereby, work load and bus bandwidth occupation on the CPU can be greatly reduced, the number of clients that can be served simultaneously by a single server can be increased, and the related power consumption can be substantially reduced.

Abstract: A method for delivering media from a server to a client device over a network is disclosed. An Virtual-Reality (VR) scene application running on the server generates a virtual VR 3D environment containing 3D models. The server checks the status of each 3D model in a predetermined order, and then, only those 3D models which are not pre-stored in the client device will all be rendered by the server into a left eye frame and a right eye frame of 2D video stream. The server then sends to the client device the frames and meta data of the 3D models which are pre-stored in the client device via the network. The client device uses a combined VR frame of these frames as a background for rendering the 3D models which are pre-stored in the client device so as to generate a mixed VR frame of video stream for output.

Abstract: A method for delivering media from a server to a client device over a network is disclosed. An Virtual-Reality (VR) scene application running on the server generates a virtual VR 3D environment containing 3D models. The server checks the status of each 3D model in a predetermined order, and then, only those 3D models which are not pre-stored in the client device will all be rendered by the server into a left eye frame and a right eye frame of 2D video stream. The server then sends to the client device the frames and meta data of the 3D models which are pre-stored in the client device via the network. The client device uses a combined VR frame of these frames as a background for rendering the 3D models which are pre-stored in the client device so as to generate a mixed VR frame of video stream for output.

Abstract: A method for delivering media from a server to a client device over a network is disclosed. An application running on the server generates a virtual 3D environment containing 3D models and sounds. The server checks the status of each 3D model or sound in a predetermined order, and then, only those 3D models and sounds which are not pre-stored in the client device will all be rendered by the server into a frame of 2D video stream with sound. The server then sends to the client device the frame and meta data of the 3D models and sounds which are pre-stored in the client device via the network. The client device uses the frame as a background for rendering the 3D models and sounds which are pre-stored in the client device so as to generate a mixed frame of video stream with sound for output.

Abstract: A method for delivering media from a server to a client device over a network is disclosed. An application running on the server generates a virtual 3D environment containing 3D models and sounds. The server checks the status of each 3D model or sound in a predetermined order, and then, only those 3D models and sounds which are not pre-stored in the client device will all be rendered by the server into a frame of 2D video stream with sound. The server then sends to the client device the frame and meta data of the 3D models and sounds which are pre-stored in the client device via the network. The client device uses the frame as a background for rendering the 3D models and sounds which are pre-stored in the client device so as to generate a mixed frame of video stream with sound for output.

Abstract: The present invention discloses a method and a system for delivering graphics from a server to a client device over a network. An application running on the server generates a virtual 3D environment containing a plurality of 3D models. The server checks the status of each 3D models in a predetermined order, and then, only those 3D models which are not pre-stored in the client device will all be rendered and encoded by the server into a frame of 2D video stream. The server then sends to the client device the frame and meta data of the 3D models which are pre-stored in the client device via the network. The client device uses the frame as a background for rendering the 3D models which are pre-stored in the client device but not included within the frame, so as to generate a mixed frame for output.

Abstract: A system and a corresponding method of leveraging GPU resources to enhance performance of the interact-able content browsing (ICB) service are to utilize the computation capacity of the GPU of the graphics card to share the computation load originally taken by the CPU. The raw images depicted by the browser application program executed in the server can be directly retrieved from the VRAM of the graphics card by the encoder inside the GPU to be further encoded and streamed before being forwarded directly to distant clients.

Abstract: A system and a corresponding method of leveraging GPU resources to enhance performance of the interact-able content browsing (ICB) service are to utilize the computation capacity of the GPU of the graphics card to share the computation load originally taken by the CPU. The raw images depicted by the browser application program executed in the server can be directly retrieved from the VRAM of the graphics card by the encoder inside the GPU to be further encoded and streamed before being forwarded directly to distant clients. Thereby, work load and bus bandwidth occupation on the CPU can be greatly reduced, the number of clients that can be served simultaneously by a single server can be increased, and the related power consumption can be substantially reduced.