Abstract: An improved loss recovery method for coding streaming media classifies each data unit in the media stream as an independent data unit (I unit), a remotely predicted unit (R unit) or a predicted data unit (P unit). Each of these units is organized into independent segments having an I unit, multiple P units and R units interspersed among the P units. The beginning of each segment is the start of a random access point, while each R unit provides a loss recovery point that can be placed independently of the I unit. This approach separates the random access point from the loss recovery points provided by the R units, and makes the stream more impervious to data losses without substantially impacting coding efficiency. The most important data units are transmitted with the most reliability to ensure that the majority of the data received by the client is usable. The I units are the least sensitive to transmission losses because they are coded using only their own data.

Abstract: An improved loss recovery method for coding streaming media classifies each data unit in the media stream as an independent data unit (I unit), a remotely predicted unit (R unit) or a predicted data unit (P unit). Each of these units is organized into independent segments having an I unit, multiple P units and R units interspersed among the P units. The beginning of each segment is the start of a random access point, while each R unit provides a loss recovery point that can be placed independently of the I unit. This approach separates the random access point from the loss recovery points provided by the R units, and makes the stream more impervious to data losses without substantially impacting coding efficiency. The most important data units are transmitted with the most reliability to ensure that the majority of the data received by the client is usable. The I units are the least sensitive to transmission losses because they are coded using only their own data.

Abstract: An improved loss recovery method for coding streaming media classifies each data unit in the media stream as an independent data unit (I unit), a remotely predicted unit (R unit) or a predicted data unit (P unit). Each of these units is organized into independent segments having an I unit, multiple P units and R units interspersed among the P units. The beginning of each segment is the start of a random access point, while each R unit provides a loss recovery point that can be placed independently of the I unit. This approach separates the random access point from the loss recovery points provided by the R units, and makes the stream more impervious to data losses without substantially impacting coding efficiency. The most important data units are transmitted with the most reliability to ensure that the majority of the data received by the client is usable. The I units are the least sensitive to transmission losses because they are coded using only their own data.

Abstract: An improved loss recovery method for coding streaming media classifies each data unit in the media stream as an independent data unit (I unit), a remotely predicted unit (R unit) or a predicted data unit (P unit). Each of these units is organized into independent segments having an I unit, multiple P units and R units interspersed among the P units. The beginning of each segment is the start of a random access point, while each R unit provides a loss recovery point that can be placed independently of the I unit. This approach separates the random access point from the loss recovery points provided by the R units, and makes the stream more impervious to data losses without substantially impacting coding efficiency. The most important data units are transmitted with the most reliability to ensure that the majority of the data received by the client is usable. The I units are the least sensitive to transmission losses because they are coded using only their own data.

Abstract: An improved loss recovery method for coding streaming media classifies each data unit in the media stream as an independent data unit (I unit), a remotely predicted unit (R unit) or a predicted data unit (P unit). Each of these units is organized into independent segments having an I unit, multiple P units and R units interspersed among the P units. The beginning of each segment is the start of a random access point, while each R unit provides a loss recovery point that can be placed independently of the I unit. This approach separates the random access point from the loss recovery points provided by the R units, and makes the stream more impervious to data losses without substantially impacting coding efficiency. The most important data units are transmitted with the most reliability to ensure that the majority of the data received by the client is usable. The I units are the least sensitive to transmission losses because they are coded using only their own data.

Abstract: A method and apparatus for cache management of distributed objects have been disclosed. A query strategy, based on a fetch strategy and a merge strategy, may be used to query a database and reconcile data in a local cache. In one approach, a merge action depends upon whether an entity was added, deleted, or changed relative to the original version of the entity as it was last fetched.

Abstract: An improved loss recovery method for coding streaming media classifies each data unit in the media stream as an independent data unit (I unit), a remotely predicted unit (R unit) or a predicted data unit (P unit). Each of these units is organized into independent segments having an I unit, multiple P units and R units interspersed among the P units. The beginning of each segment is the start of a random access point, while each R unit provides a loss recovery point that can be placed independently of the I unit. This approach separates the random access point from the loss recovery points provided by the R units, and makes the stream more impervious to data losses without substantially impacting coding efficiency. The most important data units are transmitted with the most reliability to ensure that the majority of the data received by the client is usable. The I units are the least sensitive to transmission losses because they are coded using only their own data.

Abstract: An improved loss recovery method for coding streaming media classifies each data unit in the media stream as an independent data unit (I unit), a remotely predicted unit (R unit) or a predicted data unit (P unit). Each of these units is organized into independent segments having an I unit, multiple P units and R units interspersed among the P units. The beginning of each segment is the start of a random access point, while each R unit provides a loss recovery point that can be placed independently of the I unit. This approach separates the random access point from the loss recovery points provided by the R units, and makes the stream more impervious to data losses without substantially impacting coding efficiency. The most important data units are transmitted with the most reliability to ensure that the majority of the data received by the client is usable. The I units are the least sensitive to transmission losses because they are coded using only their own data.

Abstract: An improved loss recovery method for coding streaming media classifies each data unit in the media stream as an independent data unit (I unit), a remotely predicted unit (R unit) or a predicted data unit (P unit). Each of these units is organized into independent segments having an I unit, multiple P units and R units interspersed among the P units. The beginning of each segment is the start of a random access point, while each R unit provides a loss recovery point that can be placed independently of the I unit. This approach separates the random access point from the loss recovery points provided by the R units, and makes the stream more impervious to data losses without substantially impacting coding efficiency. The most important data units are transmitted with the most reliability to ensure that the majority of the data received by the client is usable. The I units are the least sensitive to transmission losses because they are coded using only their own data.

Abstract: An adaptive entropy coder is coupled with a localized conditioning context to provide efficient compression of images with localized high frequency variations. In one implementation, an arithmetic coder can be used as the adaptive entropy coder. The localized conditioning context includes a basic context region with multiple context pixels that are adjacent the current pixel, each of the context pixels having an image tone. A state is determined for the basic context region based upon a pattern of unique image tones among the context pixels therein. An extended context region that includes the basic context region is used to identify a non-local trend within the context pixels and a corresponding state. A current pixel may be arithmetically encoded according to a previously encoded pixel having the same tone or as a not-in-context element. In one implementation, a not-in-context element may be represented by a tone in a color cache that is arranged as an ordered list of most recent not-in-context values.

Abstract: An adaptive entropy coder is coupled with a localized conditioning context to provide efficient compression of images with localized high frequency variations. In one implementation, an arithmetic coder can be used as the adaptive entropy coder. The localized conditioning context includes a basic context region with multiple context pixels that are adjacent the current pixel, each of the context pixels having an image tone. A state is determined for the basic context region based upon a pattern of unique image tones among the context pixels therein. An extended context region that includes the basic context region is used to identify a non-local trend within the context pixels and a corresponding state. A current pixel may be arithmetically encoded according to a previously encoded pixel having the same tone or as a not-in-context element. In one implementation, a not-in-context element may be represented by a tone in a color cache that is arranged as an ordered list of most recent not-in-context values.

Abstract: An adaptive entropy coder is coupled with a localized conditioning context to provide efficient compression of images with localized high frequency variations. In one implementation, an arithmetic coder can be used as the adaptive entropy coder. The localized conditioning context includes a basic context region with multiple context pixels that are adjacent the current pixel, each of the context pixels having an image tone. A state is determined for the basic context region based upon a pattern of unique image tones among the context pixels therein. An extended context region that includes the basic context region is used to identify a non-local trend within the context pixels and a corresponding state. A current pixel may be arithmetically encoded according to a previously encoded pixel having the same tone or as a not-in-context element. In one implementation, a not-in-context element may be represented by a tone in a color cache that is arranged as an ordered list of most recent not-in-context values.

Abstract: An improved loss recovery method for coding streaming media classifies each data unit in the media stream as an independent data unit (I unit), a remotely predicted unit (R unit) or a predicted data unit (P unit). Each of these units is organized into independent segments having an I unit, multiple P units and R units interspersed among the P units. The beginning of each segment is the start of a random access point, while each R unit provides a loss recovery point that can be placed independently of the I unit. This approach separates the random access point from the loss recovery points provided by the R units, and makes the stream more impervious to data losses without substantially impacting coding efficiency. The most important data units are transmitted with the most reliability to ensure that the majority of the data received by the client is usable. The I units are the least sensitive to transmission losses because they are coded using only their own data.

Abstract: An adaptive entropy coder is coupled with a localized conditioning context to provide efficient compression of images with localized high frequency variations. In one implementation, an arithmetic coder can be used as the adaptive entropy coder. The localized conditioning context includes a basic context region with multiple context pixels that are adjacent the current pixel, each of the context pixels having an image tone. A state is determined for the basic context region based upon a pattern of unique image tones among the context pixels therein. An extended context region that includes the basic context region is used to identify a non-local trend within the context pixels and a corresponding state. A current pixel may be arithmetically encoded according to a previously encoded pixel having the same tone or as a not-in-context element. In one implementation, a not-in-context element may be represented by a tone in a color cache that is arranged as an ordered list of most recent not-in-context values.

Abstract: An adaptive entropy coder is coupled with a localized conditioning context to provide efficient compression of images with localized high frequency variations. In one implementation, an arithmetic coder can be used as the adaptive entropy coder. The localized conditioning context includes a basic context region with multiple context pixels that are adjacent the current pixel, each of the context pixels having an image tone. A state is determined for the basic context region based upon a pattern of unique image tones among the context pixels therein. An extended context region that includes the basic context region is used to identify a non-local trend within the context pixels and a corresponding state. A current pixel may be arithmetically encoded according to a previously encoded pixel having the same tone or as a not-in-context element. In one implementation, a not-in-context element may be represented by a tone in a color cache that is arranged as an ordered list of most recent not-in-context values.

Abstract: An adaptive entropy coder is coupled with a localized conditioning context to provide efficient compression of images with localized high frequency variations. In one implementation, an arithmetic coder can be used as the adaptive entropy coder. The localized conditioning context includes a basic context region with multiple context pixels that are adjacent the current pixel, each of the context pixels having an image tone. A state is determined for the basic context region based upon a pattern of unique image tones among the context pixels therein. An extended context region that includes the basic context region is used to identify a non-local trend within the context pixels and a corresponding state. A current pixel may be arithmetically encoded according to a previously encoded pixel having the same tone or as a not-in-context element. In one implementation, a not-in-context element may be represented by a tone in a color cache that is arranged as an ordered list of most recent not-in-context values.

Abstract: An improved loss recovery method for coding streaming media classifies each data unit in the media stream as an independent data unit (I unit), a remotely predicted unit (R unit) or a predicted data unit (P unit). Each of these units is organized into independent segments having an I unit, multiple P units and R units interspersed among the P units. The beginning of each segment is the start of a random access point, while each R unit provides a loss recovery point that can be placed independently of the I unit. This approach separates the random access point from the loss recovery points provided by the R units, and makes the stream more impervious to data losses without substantially impacting coding efficiency. The most important data units are transmitted with the most reliability to ensure that the majority of the data received by the client is usable. The I units are the least sensitive to transmission losses because they are coded using only their own data.

Abstract: An improved loss recovery method for coding streaming media classifies each data unit in the media stream as an independent data unit (I unit), a remotely predicted unit (R unit) or a predicted data unit (P unit). Each of these units is organized into independent segments having an I unit, multiple P units and R units interspersed among the P units. The beginning of each segment is the start of a random access point, while each R unit provides a loss recovery point that can be placed independently of the I unit. This approach separates the random access point from the loss recovery points provided by the R units, and makes the stream more impervious to data losses without substantially impacting coding efficiency. The most important data units are transmitted with the most reliability to ensure that the majority of the data received by the client is usable. The I units are the least sensitive to transmission losses because they are coded using only their own data.