Patent Abstract:
A method for evaluating an end-user&#39;s subjective assessment of streaming media quality includes obtaining reference data characterizing the media stream, and obtaining altered data characterizing the media stream after the media stream has traversed a channel that includes a network. An objective measure of the QOS of the media stream is then determined by comparing the reference data and the altered data.

Full Description:
This application is a continuation application of and claims priority under 35 U.S.C. § 120 to application Ser. No. 09/870,366, filed May 30, 2001 now U.S. Pat. No. 7,020,093. Application Ser. No. 09/870,366 is incorporated herein by reference. 

   FIELD OF INVENTION 
   This invention relates to delivery of streaming media. 
   BACKGROUND 
   Streaming media refers to content, typically audio, video, or both, that is intended to be displayed to an end-user as it is transmitted from a content provider. Because the content is being viewed in real-time, it is important that a continuous and uninterrupted stream be provided to the user. The extent to which a user perceives an uninterrupted stream that displays uncorrupted media is referred to as the “Quality of Service”, or QOS, of the system. 
   A content delivery service typically evaluates its QOS by collecting network statistics and inferring, on the basis of those network statistics, the user&#39;s perception of a media stream. These network statistics include such quantities as packet loss and latency that are independent on the nature of the content. The resulting evaluation of QOS is thus content-independent. 

   
     BRIEF DESCRIPTION OF THE FIGURES 
       FIGS. 1 and 2  show content delivery systems. 
   

   DETAILED DESCRIPTION 
   As shown in  FIG. 1 , a content delivery system  10  for the delivery of a media stream  12  from a content server  14  to a client  16  includes two distinct processes. Because a media stream requires far more bandwidth than can reasonably be accommodated on today&#39;s networks, it is first passed through an encoder  18  executing on the content server  14 . The encoder  18  transforms the media stream  12  into a compressed form suitable for real-time transmission across a global computer network  22 . The resulting encoded media stream  20  then traverses the global computer network  22  until it reaches the client  16 . Finally, a decoder  24  executing on the client  16  transforms the encoded media stream  20  into a decoded media stream  26  suitable for display. 
   In the content delivery system  10  of  FIG. 1 , there are at least two mechanisms that can impair the media stream. First, the encoder  18  and decoder  24  can introduce errors. For example, many encoding processes discard high-frequency components of an image in an effort to compress the media stream  12 . As a result, the decoded media stream  26  may not be a replica of the original media stream  12 . Second, the vagaries of network transmission, many of which are merely inconvenient when text or static images are delivered, can seriously impair the real-time delivery of streaming media. 
   These two impairment mechanisms, hereafter referred to as encoding error and transmission error, combine to affect the end-user&#39;s subjective experience in viewing streaming media. However, the end-user&#39;s subjective experience also depends on one other factor thus far not considered: the content of the media stream  12  itself. 
   The extent to which a particular error affects an end-user&#39;s enjoyment of a decoded media stream  26  depends on certain features of the media stream  12 . For example, a media stream  12  rich in detail will suffer considerably from loss of sharpness that results from discarding too many high frequency components. In contrast, the same loss of sharpness in a media stream  12  rich in impressionist landscapes will scarcely be noticeable. 
   Referring to  FIG. 2 , a system  28  incorporating the invention includes a content-delivery server  30  in data communication with a client  32  across a global computer network  34 . The system  28  also includes an aggregating server  36  in data communication with both the client  32  and the content-delivery server  30 . The link between the aggregating server  36  and the client  32  is across the global computer network  34 , whereas the link between the aggregating server  36  and the content-delivery server  30  is typically over a local area network. 
   An encoder  38  executing on the content-delivery server  30  applies an encoding or compression algorithm to the original media stream  39 , thereby generating an encoded media stream  40 . For simplicity,  FIG. 2  is drawn with the output of the encoder  38  leading directly to the global computer network  34 , as if encoding occurred in real-time. Although it is possible, and sometimes desirable, to encode streaming media in real-time (for example in the case of video-conferencing applications), in most cases encoding is carried out in advance. In such cases, the encoded media  40  is stored on a mass-storage system (not shown) associated with the content-delivery server  30 . 
   A variety of encoding processes are available. In many cases, these encoding processes are lossy. For example, certain encoding processes will discard high-frequency components of an image under the assumption that, when the image is later decoded, the absence of those high-frequency components will not be apparent to the user. Whether this is indeed the case will depend in part on the features of the image. 
   In addition to being transmitted to the client  32  over the global computer network  34 , the encoded media  40  at the output of the encoder  38  is also provided to the input of a first decoder  42 , shown in  FIG. 2  as being associated with the aggregating server  36 . The first decoder  42  recovers the original media stream to the extent that the possibly lossy encoding performed by the encoder  38  makes it possible to do so. 
   The output of the decoding process is then provided to a first feature extractor  44 , also executing on the aggregating server  36 . The first feature extractor  44  implements known feature extraction algorithms for extracting temporal or spatial features of the encoded media  40 . Known feature extraction methods include the Sarnoff JND (“Just Noticeable Difference”) method and the methods disclosed in ANSI T1.801.03-1996 (“American National Standard for Telecommunications—Digital Transport of One Way Video Signals—Parameters for Objective Performance Specification”) specification. 
   A typical feature-extractor might evaluate a discrete cosine transform (“DCT”) of an image or a portion of an image. The distribution of high and low frequencies in the DCT would provide an indication of how much detail is in any particular image. Changes in the distribution of high and low frequencies in DCTs of different images would provide an indication of how rapidly images are changing with time, and hence how much “action” is actually in the moving image. 
   The original media  39  is also passed through a second feature extractor  46  identical to the first feature extractor  44 . The outputs of the first and second feature extractors  44 ,  46  are then compared by a first analyzer  48 . This comparison results in the calculation of an encoding metric indicative of the extent to which the subjective perception of a user would be degraded by the encoding and decoding algorithms by themselves. 
   An analyzer compares DCTs of two images, both of which are typically matrix quantities, and maps the difference to a scalar. The output of the analyzer is typically a dimensionless quantity between 0 and 1 that represents a normalized measure of how different the frequency distribution of two images are. 
   The content-delivery server  30  transmits the encoded media  40  to the user by placing it on the global computer network  34 . Once on the global computer network  34 , the encoded media  40  is subjected to the various difficulties that are commonly encountered when transmitting data of any type on such a network  34 . These include jitter, packet loss, and packet latency. In one embodiment, statistics on these and other measures of transmission error are collected by a network performance monitor  52  and made available to the aggregating server  36 . 
   The media stream received by the client  32  is then provided to a second decoder  54  identical to the first decoder  42 . A decoded stream  56  from the output of the second decoder  54  is made available for display to the end-user. In addition, the decoded stream  56  is passed through a third feature extractor  58  identical to the first and second feature extractors  44 ,  46 . The output of the third feature extractor  58  is provided to a second analyzer  60 . 
   The inputs to both the first and third feature extractor  44 ,  58  have been processed by the same encoder  38  and by identical decoders  42 ,  54 . However, unlike the input to the third feature extractor  58 , the input to the first feature extractor  44  was never transported across the network  34 . Hence, any difference between the outputs of the first and third feature extractors  44 ,  58  can be attributed to transmission errors alone. This difference is determined by second analyzer  60 , which compares the outputs of the first and third feature extractors  44 ,  58 . On the basis of this difference, the second analyzer  60  calculates a transmission metric indicative of the extent to which the subjective perception of a user would be degraded by the transmission error alone. 
   The system  28  thus provides an estimate of a user&#39;s perception of the quality of a media stream on the basis of features in the rendered stream. This estimate is separable into a first portion that depends only on encoding error and a second portion that depends only on transmission error. 
   Having determined a transmission metric, it is useful to identify the relative effects of different types of transmission errors on the transmission metric. To do so, the network statistics obtained by the network performance monitor  52  and the transmission metric determined by the second analyzer  60  are provided to a correlator  62 . The correlator  62  can then correlate the network statistics with values of the transmission metric. The result of this correlation identifies those types of network errors that most significantly affect the end-user&#39;s experience. 
   In one embodiment, the correlator  62  averages network statistics over a fixed time-interval and compares averages thus generated with corresponding averages of transmission metrics for that time-interval. This enables the correlator  62  to establish, for that time interval, contributions of specific network impairments, such as jitter, packet loss, and packet latency, toward the end-user&#39;s experience. 
   Although the various processes are shown in  FIG. 1  as executing on specific servers, this is not a requirement. For example, the system  28  can also be configured so that the first decoder  42  executes on the content-delivery server  30  rather than on the aggregating server  36  as shown in  FIG. 1 . In one embodiment, the output of the first feature extractor is sent to the client and the second analyzer executes at the client rather than at the aggregating server  36 . The server selected to execute a particular process depends, to a great extent, on load balancing. 
   Other embodiments are within the scope of the following claims.

Technology Classification (CPC): 7