Efficient coding complexity estimation for video transcoding systems

Systems and methods of transcoding video bitstreams that employ highly efficient look-ahead approaches to video transcoding. The systems and methods perform video transcoding in the pixel domain to make successive adjustments to estimates of the coding complexity of video frames in input video bitstreams. The systems and methods can employ the adjusted estimates of the coding complexity in a rate control function to provide improved bit allocations for the video frames in transcoded output video bitstreams, thereby enhancing overall perceptual quality.

CROSS REFERENCE TO RELATED APPLICATIONS

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FIELD OF THE INVENTION

The present application relates generally to systems and methods of transcoding video bitstreams, and more specifically to systems and methods of transcoding video bitstreams compressed according to coding formats such as H.263, H.264, and MPEG-4.

BACKGROUND OF THE INVENTION

In recent years, there has been an increasing need for systems and methods of transcoding video bitstreams, due in no small part to the growing diversity of available multimedia applications, multimedia networks, video coding standards, video displays, etc. For example, the H.264 video coding standard has provided significant enhancements in coding efficiency over earlier video coding standards, and has been widely employed in multimedia applications such as real-time video communications and video streaming. Because multimedia applications such as video streaming generally allow significant delays (e.g., up to 5 seconds or more) to be incorporated in the video bitstream transcoding process, conventional video transcoding systems have typically employed a so-called “look-ahead” approach to video transcoding, taking time to analyze “future” video frames in the video bitstream to provide improved bit allocations for the video frames currently being transcoded, thereby enhancing overall perceptual quality. In general, for video transcoding systems employing the look-ahead approach, the bit allocation for a current video frame typically improves as the number of future video frames available for analysis increases.

Conventional video transcoding systems employing look-ahead approaches to video transcoding have drawbacks, however, in that the system resources may strictly limit the number of future video frames that can be stored and analyzed. For example, in a look-ahead approach that allows at least 5 seconds of delay, a conventional video transcoding system receiving an input video bitstream at 30 frames per second (fps) would need to store at least 150 video frames for subsequent analysis before any video frames in the input bitstream could be transcoded. Nevertheless, constraints imposed by the system resources may make such storage of video frames by the conventional video transcoding system impractical, if not impossible.

It would therefore be desirable to have improved systems and methods of transcoding video bitstreams that avoid the drawbacks of conventional video transcoding systems.

BRIEF SUMMARY OF THE INVENTION

These are other objects can be accomplished, in accordance with the present application, by systems and methods of transcoding video bitstreams that employ highly efficient look-ahead approaches to video transcoding. Such systems and methods of transcoding video bitstreams are configured to operate in the pixel domain, making successive adjustments to estimates of the coding complexity of video frames in input video bitstreams, and employing the adjusted estimates of the coding complexity in rate control functions to provide improved bit allocations for video frames in transcoded output video bitstreams, thereby enhancing overall perceptual quality.

In accordance with one aspect, a system for transcoding video bitstreams (also referred to herein as a/the “video transcoding system”) includes a bitstream analyzer having a data collection component and a coding complexity estimation component, a video decoder, and a video encoder. In accordance with one exemplary aspect, the video transcoding system is configured to operate in the pixel domain, receiving an input video bitstream (also referred to herein as a/the “input bitstream”) generated by an external video encoder that is separate from the disclosed video transcoding system. The external video encoder receives an input video sequence (also referred to herein as a/the “input sequence”) comprising a plurality of video frames, encodes the input sequence according to a predetermined coding format, such as the H.263 coding format, the H.264 coding format, the MPEG-4 coding format, or any other suitable coding format, and sends the resulting input bitstream to the video transcoding system. The video transcoding system receives the input bitstream, and provides the input bitstream to the data collection component within the bitstream analyzer, and to the video decoder of the video transcoding system. In accordance with another exemplary aspect, the data collection component performs slice layer data collection, collecting, from the input bitstream, slice layer information such as the video frame size, the number of video frames to be buffered in the bitstream analyzer, the frame type (e.g., intra-coded or predictive), the number of prediction bits and the number of texture bits for each video frame, and/or any other suitable slice layer information. The data collection component also performs macroblock (also referred to herein as “MB”) layer data collection, collecting, from the input bitstream, MB layer information such as the MB type, the quantization parameter (also referred to herein as “QP”) used to encode the MB, the number of prediction bits and the number of texture bits in each MB, and/or any other suitable MB layer information. The data collection component is operative to collect such slice layer information and MB layer information from the input bitstream, and to provide the collected information to the coding complexity estimation component within the bitstream analyzer.

The coding complexity estimation component is operative to analyze, using a coding complexity estimation technique, the slice layer information and the MB layer information provided by the data collection component, thereby generating an estimate of the coding complexity (also referred to herein as “frame complexity”) of each video frame in the input bitstream. The coding complexity estimation component is operative to provide such estimates of the frame complexity to the video encoder, which employs the estimates of the frame complexity in a rate control function to adapt the video transcoding system to the network environment in which the system is employed. Specifically, the video decoder is operative to read the plurality of video frames in the input bitstream, to decode the plurality of video frames, and to provide the decoded video frames to the video encoder, which implements the rate control function to allocate bits to the respective video frames in a transcoded output video bitstream (also referred to herein as a/the “transcoded output bitstream”).

The coding complexity estimation technique used by the coding complexity estimation component includes a coding complexity estimation function operative to receive the slice layer information and the MB layer information collected from the input bitstream by the data collection component, and to provide, to an input quality adjustment function, an initial estimate of the frame complexity of each video frame in the input bitstream based, at least in part, on the quantization parameter, QP, used to generate the input bitstream, and the coding bits included in the input bitstream. The input quality adjustment function is one of a plurality of functions for adjusting the initial estimates of the frame complexity generated by the coding complexity estimation function. In accordance with another exemplary aspect, the plurality of functions for adjusting the initial estimates of the frame complexity include the input quality adjustment function, a temporal distance adjustment function, a constant bit rate (CBR) adjustment function, and a perceptual quality adjustment function. The perceptual quality adjustment function provides the adjusted estimate of the frame complexity of each video frame to the video encoder, which employs the adjusted estimates of the frame complexity in the rate control function to provide improved bit allocations for video frames in the transcoded output bitstream, thereby enhancing overall perceptual quality.

Other features, functions, and aspects of the invention will be evident from the Drawings and/or the Detailed Description of the Invention that follow.

DETAILED DESCRIPTION OF THE INVENTION

Systems and methods of transcoding video bitstreams are disclosed that employ highly efficient look-ahead approaches to video transcoding. In accordance with the disclosed systems and methods, video transcoding is performed in the pixel domain using a coding complexity estimation technique for making successive adjustments to estimates of the coding complexity of video frames in input video bitstreams. The disclosed systems and methods employ the adjusted estimates of the coding complexity of the video frames to provide improved bit allocations for the video frames in transcoded output video bitstreams, thereby enhancing overall perceptual quality.

FIG. 1depicts an illustrative embodiment of an exemplary video transcoding system100, in accordance with the present application. The video transcoding system100includes a bitstream analyzer102, a video decoder104, and a video encoder106. Further, the bitstream analyzer102includes a data collector102.1and a coding complexity estimator102.2. In accordance with the illustrative embodiment ofFIG. 1, the video transcoding system100is configured to operate in the pixel domain, receiving an input video bitstream (also referred to herein as the “input bitstream”) generated by an external video encoder108that is separate from the video transcoding system100. Specifically, the external video encoder108receives an input video sequence (also referred to herein as the “input sequence”) comprising a plurality of video frames, encodes the input sequence according to a predetermined coding format (e.g., the H.263 coding format, the H.264 coding format, the MPEG-4 coding format, or any other suitable coding format), and sends the resulting input bitstream to the video transcoding system100. The video transcoding system100receives the input bitstream, and provides the input bitstream to the data collector102.1within the bitstream analyzer102, and to the video decoder104of the video transcoding system100.

It is noted that the data collector102.1can incorporate a predetermined number of video frames of delay in the input bitstream. For example, the data collector102.1may incorporate300video frames of delay, or any other suitable number of video frames of delay. Moreover, the data collector102.1performs slice layer data collection, collecting, from the input bitstream, slice layer information such as the video frame size, the number of video frames to be buffered in the bitstream analyzer102, the frame type (e.g., intra-coded or predictive), the number of prediction bits and the number of texture bits for each video frame, and/or any other suitable slice layer information. The data collector102.1also performs macroblock (also referred to herein as “MB”) layer data collection, collecting, from the input bitstream, MB layer information such as the MB type, the quantization parameter (also referred to herein as “QP”) used to encode each MB, the number of prediction bits and the number of texture bits in each MB, and/or any other suitable MB layer information. The data collector102.1is operative to collect such slice layer information and MB layer information from the input bitstream, and to provide the collected information to the coding complexity estimator102.2within the bitstream analyzer102.

In further accordance with the illustrative embodiment ofFIG. 1, the coding complexity estimator102.2is configured to include at least one processor operative to execute at least one program out of at least one memory to analyze, using an exemplary coding complexity estimation technique200(seeFIG. 2), the slice layer information and the MB layer information provided by the data collector102.1, thereby generating an estimate of the coding complexity (also referred to herein as “frame complexity,” or “CF”) of each video frame in the input bitstream. The coding complexity estimator102.2is operative to provide such estimates of the frame complexity, CF, to the video encoder106, which employs the estimates of the frame complexity, CF, in a rate control function to adapt the video transcoding system100to the network environment in which the system is employed. It is noted that the video encoder106can implement a slice layer rate control function, an MB layer rate control function, or any other suitable type of rate control function. For example, the video encoder106may employ the rate control function described in co-pending U.S. patent application Ser. No. 12/497,110 filed Jul. 2, 2009, entitled A BITRATE CONTROL ALGORITHM FOR VIDEO TRANSCODING SYSTEMS, which is assigned to the same assignee of the present application, and which is incorporated herein by reference in its entirety. Moreover, the video decoder104is operative to read the plurality of video frames in the input bitstream, to decode the plurality of video frames, and to provide the decoded video frames to the video encoder106, which implements the rate control function to allocate bits to the respective video frames in a transcoded output video bitstream (also referred to herein as the “transcoded output bitstream”).

The disclosed coding complexity estimation technique200is described below with reference toFIG. 2. It is noted that the coding complexity estimation technique200ofFIG. 2can be performed by the coding complexity estimator102.2within the bitstream analyzer102of the video trancoding system100(seeFIG. 1). The coding complexity estimation technique200includes a coding complexity estimation function202operative to receive the slice layer information and the MB layer information collected from the input bitstream by the data collector102.1, and to provide an initial estimate of the frame complexity, CF, of each video frame in the input bitstream to an input quality adjustment function204. The input quality adjustment function204is one of a plurality of exemplary functions for adjusting the initial estimates of the frame complexity, CF, generated by the coding complexity estimation function202. In accordance with the illustrative embodiment ofFIG. 2, the plurality of exemplary functions for adjusting the initial estimates of the frame complexity, CF, include, but are not limited to, the input quality adjustment function204, a temporal distance adjustment function206, a constant bit rate (CBR) adjustment function208, and a perceptual quality adjustment function210. It is noted that any other suitable function or functions for adjusting the estimates of the frame complexity, CF, may be employed in addition to, or in place of, some or all of the exemplary functions204,206,208, and210. The perceptual quality adjustment function210provides the adjusted estimate of the frame complexity, CF, of each video frame to the video encoder106of the video transcoding system100.

The coding complexity estimation function202, and the plurality of exemplary functions204,206,208,210(seeFIG. 2) for adjusting the initial estimates of the frame complexity, CF, generated by the coding complexity estimation function202, are described below with reference to a plurality of exemplary input video sequences. The coding complexity estimation function202estimates the frame complexity, CF, as coding bits that the external video encoder108(seeFIG. 1) would generate while encoding a video frame in an input sequence using a predetermined reference quantization parameter (also referred to herein as “QPref—est”) instead of the quantization parameter (QP) used to encode the corresponding input bitstream in the video encoder106. In accordance with the present application, the plurality of exemplary input sequences are encoded by the external video encoder108using a predetermined range of quantization parameters (QPs), and subsequently transcoded by the video encoder106(seeFIG. 1) of the video transcoding system100. Further, because the estimates of the frame complexity, CF, generated by the coding complexity estimator102.2are used in the video encoder106, the reference quantization parameter, QPref—est, is preferably set to be equal to a value that is close to the value of the quantization parameter, QP, used in the video encoder106. To achieve this, the QPref—estvalue is calculated as the average QP of the previously encoded video frames in the video encoder106in the case of constant bit rate (CBR) coding, and is configured as a fixed QP value in the case where a constant QP is used in the video encoder106. For example, the predetermined range of QPs used by the external video encoder108, which may comprise an external H.264 video encoder, may range from about 0 to 51, or any other suitable range of QPs. Further, the reference quantization parameter QPref—est, may be set to be equal to 32, or to any other suitable QP value. Moreover, the exemplary input sequences may comprise common intermediate format (CIF) video sequences, concatenated video graphics array (VGA) video sequences, and/or any other suitable type of video sequences, including a plurality of video clips covering diverse types of video content. Such video sequences included in the exemplary input sequences may each have about 300 to 400 video frames of varying complexity, or any other suitable number of video frames having any other suitable level(s) of complexity.

Coding Complexity Estimation Function

As discussed above, the video encoder106of the video transcoding system100(seeFIG. 1) employs estimates of the frame complexity, CF, generated by the coding complexity estimator102.2in a rate control function to allocate bits to respective video frames in the transcoded output bitstream. It is noted that, whereas the external video encoder108encodes the exemplary input video sequences using the predetermined range of QPs (e.g., from about 0 to 51), the external video encoder108typically generates more coding bits (e.g., texture bits and prediction bits) as the complexity of the video frames in the input sequences increases. Moreover, for a given bit allocation, the external video encoder108typically uses larger quantization parameters, QPs, as the complexity of the respective video frames in the exemplary input sequences increases. For at least these reasons, the coding complexity estimation function202is operative to estimate the frame complexity, CF, based on the coding bits (e.g., the texture bits and the prediction bits) generated for the respective video frames, and the quantization parameter, QP, used to encode the respective video frames. For example, in the case of constant bit rate (CBR) coding, the quantization parameter, QP, used to encode a current video frame in the input bitstream can be set to be equal to the average of the quantization parameters, QPs, used to encode all of the macroblocks, MBs, in the current video frame. Moreover, in the case of constant QP coding, the quantization parameter, QP, can be set to a predetermined fixed value.

In accordance with the exemplary embodiment ofFIG. 2, the frame complexity, CF, generated by the coding complexity estimation function202for the current video frame can be represented as the sum of the complexities for all of the macroblocks, MBs, in the respective video frame, as follows,

CF⁡(Q⁢⁢P)=∑i=1num_MB⁢⁢CMB⁡[i]⁡(Q⁢⁢P),(1)
where “i” corresponds to a macroblock (MB) index ranging from 1 to the total number of MBs, num_MB, in the respective video frame, “MB[i]” is the macroblock corresponding to MB index i, “CMB [i]” represents the complexity of the macroblock, MB[i], and “QP” represents the quantization parameter used to encode the respective video frame. Further, the complexity, CMB [i], of the macroblock, MB[i], can be expressed as:
CMB[i](QP)=αMB[i](QP,TMB[i])*TMB[i]+βMB[i](QP,PMB[i])*PMB[i](2)
where “TMB[i]” represents the texture bits included in the macroblock, MB[i], “PMB[i]” represents the prediction bits included in the macroblock, MB[i], “αMB[i](QP,TMB[i])” is a weighting parameter for the texture bits, TMB[i], and “βMB[i](QP,PMB[i])” is a weighting parameter for the prediction bits, PMB[i]. It is noted that the weighting parameters, αMB[i](QP,TMB[i]) and βMB[i](QP,PMB[i]), for the texture bits, TMB[i], and the prediction bits, PMB[i], respectively, are employed in equation (2) above because the texture bits, TMB[i], and the prediction bits, PMB[i], can have significantly different characteristics.

In further accordance with the illustrative embodiment ofFIG. 2, the weighting parameters, αMB[i](QP,TMB[i]) and βMB[i](QP,PMB[i]), can be estimated as follows. Assuming that the weighting parameters, αMB[i](QP,TMB[i]) and βMB[i](QP,PMB[i]), are the same for all of the macroblocks, MB[i], in the current video frame, the frame complexity, CF, can be expressed as:

CF⁡(Q⁢⁢P)=⁢∑i=1num_MB⁢(αMB⁡[i]⁡(Q⁢⁢P,TMB⁡[i])*TMB⁡[i]+⁢βMB⁡[i]⁡(Q⁢⁢P,PMB⁡[i])*PMB⁡[i])=⁢αF⁡(Q⁢⁢P)⁢∑i=1num_MB⁢TMB⁡[i]+βF⁡(Q⁢⁢P)⁢∑i=1num_MB⁢PMB⁡[i]=⁢αF⁡(Q⁢⁢P)*TF+βF⁡(Q⁢⁢P)*PF(3)
where “TF” and “PF” represent the texture bits and prediction bits, respectively, for the respective video frame, and “αF(QP)” and “βF(QP)” represent the weighting parameters for the texture bits, TF, and the prediction bits, PF, respectively, for the respective video frame.

The weighting parameters αF(QP) and βF(QP) for the respective video frame are trained for different QPs so that the frame complexity, CF(QP), in equation (3) above becomes close to the frame complexity, CFQPref—est) where
αF(QPref—est)=βF(QPref—est)=1.0.  (4)
FIG. 3adepicts, for QPref—estbeing equal to 32, exemplary values of the weighting parameter, αF(QP), with reference to the quantization parameters, QPs, ranging from 0 to 51. Further,FIG. 3bdepicts, for QPref—estbeing equal to 32, exemplary values of the weighting parameter, βF(QP), with reference to the quantization parameters, QPs, ranging from 0 to 51.

It is noted that a user of the video transcoding system100may have little or no control over the external video encoder108that provides the input bitstream to the video transcoding system100. Because different video encoders may employ different optimization and motion estimation techniques, the texture bits, TF, and the prediction bits, PF, generated by the video encoders for the respective video frames in the input bitstream may be different from one video encoder to another. To account for such diversity in video encoders, any dependency on a specific video encoder can be essentially removed from the disclosed coding complexity estimation technique200by adjusting the weighting parameters, αF(QP) and βF(QP), using a parameter, “φ,” as follows,

It is further noted that, if a macroblock, MB, in a respective video frame has a small number of texture and prediction bits after being encoded by the external video encoder108using a relatively small quantization parameter, QP (e.g., QP=20), then the probability increases that the MB will be skipped when the MB is encoded by the same external video encoder108using a larger reference quantization parameter, OPref—est(e.g., QPref—est=32). It follows that errors in the estimates of the frame complexity, CF, of the respective video frames may increase if the skip probabilities of the macroblocks, MBs, included in the video frames are not taken into account, especially if the quantization parameter, QP, used by the external video encoder108to encode the video frames is different from the reference quantization parameter, QPref—est. For at least this reason, the texture bits, TMB[i], included in the macroblock, MB[i], are adjusted, as follows,
TMB[i]=TMB[i]*γ(QP,TMB,PMB),  (6)
where “γ(QP,TMB[i],PMB[i])” is a parameter that is dependent on the quantization parameter, QP, the texture bits, TMB[i], and the prediction bits, PMB[i].FIG. 5depicts exemplary values of the parameter, γ(QP,TMB[i],PMB[i]), with reference to a range of values designated as “table_idx,” which can be determined as follows,

To estimate the frame complexity, CF, with increased accuracy, the weighting parameters, αF(QP) and βF(QP), can be adjusted for each of the macroblocks, MBs, in the respective video frame. For example, with reference toFIG. 6a, to obtain the weighting parameter, αMB[i](QP,TMB[i]), using a piecewise linear approach, the expressions below may be employed,

In equation (9) above, the parameters, “a1” and “a2,” can be obtained using the diagram depicted inFIG. 7a, which illustrates exemplary values of the parameters, a1 and a2, with reference to the quantization parameters, QPs, ranging from 0 to 51. Further, the parameters, “b1,” “b2,” and “b3,” in equation (9) above can be obtained using the diagram depicted inFIG. 7b, which illustrates exemplary values of the parameters, b1, b2, and b3, with reference to the quantization parameters, QPs, ranging from 0 to 51.

Moreover, with reference toFIG. 6b, to obtain the weighting parameter, βMB[i](QP,PMB[i]), using the piecewise linear approach noted above, the expressions below may be employed,

By way of example, in equation (10) above, the parameters “c1,” “d1” and “d2,” may be set to be equal to 512, 0.75, and 1.05, respectively, or to any other suitable values.

Input Quality Adjustment Function

As described above, the input quality adjustment function204(seeFIG. 2) is one of a plurality of functions for adjusting the initial estimates of the frame complexity, CF, generated by the coding complexity estimation function202(seeFIG. 2). In accordance with the exemplary embodiment ofFIG. 2, the input quality adjustment function204adjusts the initial estimates of the frame complexity, CF, based on the quantization parameter, QP, used by the external video encoder108. Specifically, the input quality adjustment function204employs a parameter, “δ(QP)”, to adjust each initial estimate of the frame complexity, CF, as follows,
CF=CF*δ(QP).  (11)
FIG. 8illustrates exemplary values of the parameter, δ(QP), with reference to the quantization parameters, QPs, ranging from 0 to 51.
Temporal Distance Adjustment Function

It is noted that the estimation of the frame complexity, CF, described above has not taken into account the temporal distance between a current video frame and a predetermined reference frame for the current video frame, which is used to encode the current video frame in the external video encoder108. In general, as the temporal distance between the current video frame and its reference frame increases, the temporal correlation between the respective video frames decreases, typically resulting in the use of an increased number of bits within the external video encoder108. However, this does not necessarily lead to an increase in the frame complexity, CF, of the current video frame. In accordance with the exemplary embodiment ofFIG. 2, the temporal distance adjustment function206normalizes the frame complexity, CF, by the temporal distance between the current video frame and its reference frame to increase the accuracy of the estimate of the frame complexity, CF. Specifically, the temporal distance adjustment function206employs a parameter, “ε(distance, org_complexity),” to normalize the frame complexity, CF, as follows,
CF=CF*ε(distance,org_complexity)  (12)
where “distance” corresponds to the temporal distance between the current video frame and its reference frame, and “org_complexity” corresponds to the original complexity of the current video frame.FIG. 9illustrates exemplary values of the parameter, e (distance, org_complexity), with reference to exemplary values of the original complexity of the current video frame, and with reference to exemplary values of the temporal distance between the current video frame and its reference frame.
Constant Bit Rate (CBR) Adjustment Function

It is further noted that, in a constant bit rate (CBR) environment, video encoders may use different quantization parameters, QPs, to encode different video frames in an input video sequence. Further, such video encoders may employ predetermined reference frames that have different associated levels of quality. Accordingly, in the CBR environment, a number of observations can be made, as follows.

Observation 1—The difference between a current video frame and its reference frame in the video encoder106typically increases as the difference between the quantization parameters, QPs, used by the external video encoder108to encode the respective video frames increases. As a result, if the same number of bits generated by the external video encoder108to encode the respective video frames were used to estimate the frame complexity, CF, of a video frame of the predictive type (also referred to herein as the “P-frame”), then the frame complexity, CF, of the P-frame would likely be underestimated.

Observation 2—If the quantization parameter, QP, used for generating the reference frame was smaller than the quantization parameter, QP, used to encode the current video frame in the external video encoder108, then the number of bits generated by the external video encoder108for encoding the current video frame would likely be less than the number of bits that would have been generated if the quantization parameter, QP, was about equal to the quantization parameter, QP, used for generating the reference frame. As a result, if the same number of bits generated by the external video encoder108to encode the respective video frames were used to estimate the frame complexity, CF, of a P-frame, then the frame complexity, CF, of the P-frame would likely be underestimated.

Observation 3—If the quantization parameter, QP, used for generating the reference frame was larger than the quantization parameter, QP, used to encode the current video frame in the external video encoder108, then the number of bits generated by the external video encoder108for encoding the current video frame would likely be greater than the number of bits that would have been generated if the quantization parameter, QP, was about equal to the quantization parameter, QP, used for generating the reference frame. As a result, if the same number of bits generated by the external video encoder108to encode the respective video frames were used to estimate the frame complexity, CF, of a P-frame, then the frame complexity, CF, of the P-frame would likely be overestimated.

Taking into account the three observations discussed above, the CBR adjustment function208adjusts the frame complexity, CF, of P-frames using a parameter, “τ” as follows,
CF=CF*τ,  (13)
where the parameter, τ, is based on the difference between the quantization parameter used to generate the reference frame (also referred to herein as “ref_QP”), and the quantization parameter used to encode the current video frame (also referred to herein as “curr_QP”).FIG. 10illustrates exemplary values of the parameter, τ, with reference to a range of values for ref_QP, and with reference to a range of values for curr_QP.

It is also noted that the video encoder106of the video transcoding system100may employ different frame types (e.g., intra-coded, predictive) from those used by the external video encoder108. For example, the external video encoder108may have encoded a video frame as an intra-coded frame (also referred to herein as an/the “I-frame”), and the video encoder106may determine that the video frame is to be encoded as a P-frame. Because the complexity of the I-frame generated by the external video encoder108may be significantly different from the complexity of the P-frame generated by the video encoder106, the CBR adjustment function208adjusts the frame complexity, CF, of the current P-frame based on the frame complexity, CF, of the previous P-frame and the frame complexity, CF, of the next P-frame, as follows,
CF(current_frame)= 15/32*CF(previous_frame)+ 1/16*CF(current_frame)+ 15/32*CF(next_frame)  (14)
where “CF(current_frame)” represents the frame complexity, CF, of the current P-frame, “CF(previous_frame)” represents the frame complexity, CF, of the previous P-frame, and “CF(next_frame)” represents the frame complexity, CF, of the next P-frame. It is noted that, in equation (14) above, the weights given to the previous P-frame, CF(previous_frame), the current P-frame, CF(current_frame), and the next P-frame, CF(next_frame), may be 15/32, 1/16, and 15/32, respectively, or any other suitable weighting values.

Moreover, the external video encoder108may have encoded a video frame as a P-frame, and the video encoder106may determine that the video frame is to be encoded as an I-frame. For example, the external video encoder108may have failed to detect a scene change in a video sequence including the video frame and therefore encoded that video frame as a P-frame, while the video encoder106may have successfully detected the scene change and therefore encoded that video frame as an I-frame. In this case, the CBR adjustment function208estimates the frame complexity, CF, of such an I-frame, as follows,
I_complexity=P_complexity*θ*υ+I_complexity_prev*(1−υ)  (15)
where “I_complexity” represents the frame complexity, CF, of the current I-frame, “P_complexity” represents the frame complexity, CF, of the corresponding P-frame, and “I_complexity_prev” represents the frame complexity, CF, of the previous video frame encoded as the I-frame in the external video encoder108. Further, “θ” is a first parameter for use in adjusting the complexity of the corresponding P-frame, P_complexity, and “υ” is a second parameter for use in weighing the complexity of the corresponding P-frame, P_complexity, and the complexity of the previous video frame encoded as the I-frame, I_complexity_prev. It is noted that the parameter, υ, provides an indication of the reliability of the content of the previous I-frame. In accordance with the exemplary embodiment ofFIG. 2, each of the parameters, θ and υ, can be determined, from the encoding history, by a ratio of the intra-coded macroblocks, MBs, in the respective I-frames.
Perceptual Quality Adjustment Function

To enhance perceptual quality, the weighting parameters, αMB[i](QP,TMB[i]), βMB[i](QP,PMB[i]) and αF(QP), βF(QP), described above can be configuredto allow more bits to be allocated to those video frames having associated levels of distortion that would likely be visually perceptible by a typical human user. Because the typical human user can visually detect distortion in simple scenes with relative ease, the perceptual quality adjustment function210is configured to increase the smaller frame complexity values, and to reduce the larger frame complexity values, using a parameter, “μ,” as follows.
CF=CF*μ  (16)
FIG. 11illustrates exemplary values of the parameter, μ, with reference to exemplary values of the frame complexity, CF. Moreover, to avoid allocating either too few bits or too many bits to the respective video frames, which can be potentially problematic while the video encoder106performs the rate control function, suitable minimum and maximum values can be specified for the values of the frame complexity, CF.

An illustrative method of transcoding video bitstreams, in accordance with the present application, is described below with reference toFIGS. 1,2, and12. As depicted in step1202(seeFIG. 12), the data collector102.1within the bitstream analyzer102(seeFIG. 1) receives an input video bitstream including a plurality of video frames from an input video sequence. Each of the plurality of video frames in the input video bitstream is encoded in a first coding format using a first quantization parameter (QP). As depicted in step1204, for each of the plurality of video frames in the input video bitstream, the coding complexity estimator102.2within the bitstream analyzer102(seeFIG. 1) generates an estimate of the coding complexity of the respective video frame. As depicted in step1206, for each of the plurality of video frames in the input video bitstream, the coding complexity estimator102.2employs the input quality adjustment function204(seeFIG. 2) to adjust the estimate of the coding complexity of the respective video frame based on the first QP used to encode the respective video frame in the first coding format. Further, as depicted in step1208, for each of the plurality of video frames in the input video bitstream, the coding complexity estimator102.2employs the temporal distance adjustment function206(seeFIG. 2) to normalize the estimate of the coding complexity of the respective video frame based on a temporal distance between the respective video frame and its reference frame. As depicted in step1210, for each of the plurality of video frames in the input video bitstream, the coding complexity estimator102.2employs the CBR adjustment function208(seeFIG. 2) to adjust the estimate of the coding complexity of the respective video frame based on the difference between the first QP, and a second QP used to encode its reference frame in the first coding format. Moreover, as depicted in step1212, for each of the plurality of video frames in the input video bitstream, if the respective video frame is subsequently encoded in the video encoder106(seeFIG. 1) in a second coding format, and in a frame type that is different from the frame type of the respective video frame encoded in the first coding format, then the coding complexity estimator102.2employs the CBR adjustment function208to adjust the estimate of the coding complexity of the respective video frame based on the coding complexity of the previous video frame in the input video bitstream, and on the coding complexity of the next video frame in the input video bitstream. It is noted that the second coding format may be the same as, or different from, the first coding format. In addition, as depicted in step1214, for each of the plurality of video frames in the input video bitstream, if the coding complexity of the respective video frame is perceived to be high, then the coding complexity estimator102.2employs the perceptual quality adjustment function210to reduce the high coding complexity of the respective video frame to be less than a predetermined maximum value. Further, as depicted in step1216, for each of the plurality of video frames in the input video bitstream, if the coding complexity of the respective video frame is perceived to be low, then the coding complexity estimator102.2employs the perceptual quality adjustment function210to increase the low coding complexity of the respective video frame to be greater than a predetermined minimum value. As depicted in step1218, the video decoder104(seeFIG. 1) decodes the plurality of video frames encoded in the first coding format, thereby generating a plurality of decoded video frames. As depicted in step1220, the video encoder106encodes the plurality of decoded video frames in the second coding format, thereby generating a transcoded output video bitstream. As depicted in step1222, the video encoder106uses the estimates of the coding complexity of the respective video frames in a rate control function to determine bit allocations for the respective video frames in the transcoded output video bitstream.

EXAMPLES

The disclosed systems and methods of transcoding video bitstreams will be further understood with reference to the following illustrative, non-limiting examples and FIGS.1and13-15. In a first illustrative example, 18 exemplary input video sequences, each comprising a plurality of video frames, are applied to the external video encoder108(seeFIG. 1). The respective exemplary input video sequences include two exemplary sequences, specifically, a CIF sequence and a concatenated VGA sequence. Moreover, three different video encoders for encoding the exemplary input video sequences according to the H.264 coding format are employed as the external video encoder108, including a JM 16.2 video encoder, an x264 video encoder, and an IPP H.264 video encoder. The CIF sequence is encoded by the respective external video encoders at 128 Kbps, 192 Kbps, and 300 Kbps, and the VGA sequence is encoded by the respective external video encoders at 350 Kbps, 650 Kbps, and 1.0 Mbps.

FIG. 13depicts estimated values of the frame complexity, CF, generated by the coding complexity estimator102.2(seeFIG. 1) using the disclosed coding complexity estimation technique200(seeFIG. 2), for the respective exemplary input video sequences. Using these estimates of the frame complexity, CF, and the reference quantization parameter, QPref—est, to be equal to 32, input bitstreams are encoded by the video encoder106to obtain reference values of the frame complexity, CF. In accordance with this first illustrative example, it can be observed that the bias between the estimated values of the frame complexity, CF, and the reference values of the frame complexity, CF, is less than 2%, and the average of the absolute error for each video frame is about 16%. Moreover, the overhead in speed using the disclosed coding complexity estimation technique200is about 3.7% for the input bitstreams.

It was described above that the coding complexity estimator102.2provides estimates of the frame complexity, CF, to the video encoder106, which can employ the estimates of the frame complexity, CF, in a rate control function to adapt the video transcoding system100to the network environment in which the system is employed. In a second illustrative example, the video encoder106is configured as follows—IPPPP, H.264 to H.264 transcoding, the frame rate equal to 30 fps, the bit rate equal to 300 Kbps, High profile, and CABAC entropy coding. In this exemplary configuration, the video encoder106implements the rate control function to allocate bits to the respective video frames in the input bitstreams.

FIG. 14depicts the frame bit allocation resulting from the execution of the rate control function by the video encoder106, using estimates of the frame complexity, CF, obtained by both a conventional coding complexity estimation technique and the coding complexity estimation technique200that is disclosed in accordance with the present application. As depicted inFIG. 14, when the conventional coding complexity estimation technique is employed, the video encoder106allocates a similar amount of bits to each video frame, based on a specified virtual buffer status and a specified target bitrate, regardless of the frame complexity, CF. However, when the disclosed coding complexity estimation technique200is employed,FIG. 14depicts that the video encoder106uses actual values of the frame complexity, CF, to allocate a larger number of bits to the more complicated video frames, and to allocate a smaller number of bits to the less complicated video frames.

FIG. 15depicts the quantization parameter, QP, distribution resulting from the execution of the rate control function by the video encoder106, using the estimates of the frame complexity, CF, obtained by the same conventional coding complexity estimation technique used inFIG. 14, and the disclosed coding complexity estimation technique200(seeFIG. 2). As depicted inFIG. 15, for the disclosed coding complexity estimation technique200, the QP fluctuations are much smoother than those obtained when the conventional coding complexity estimation technique is employed, thereby enhancing overall perceptual quality. Because the video encoder106uses the actual values of the frame complexity, CF, while employing the disclosed coding complexity estimation technique200, a larger number of bits are allocated to the more complicated video frames, and a smaller number of bits are allocated to the less complicated video frames, thereby resulting in a generally smoother distribution of the quantization parameter, QP.

It is noted that the operations performed by the above-described illustrative embodiments are purely exemplary and imply no particular order. Further, these operations can be used in any sequence when appropriate and/or can be partially used. With the above embodiments in mind, it should be understood that the above-described systems and methods can employ various computer-implemented operations involving data stored in computer systems. These operations are those requiring physical manipulation of physical quantities. Usually, though not necessarily, these quantities take the form of electrical, magnetic, or optical signals capable of being stored, transferred, combined, compared, or otherwise manipulated.

Moreover, any of the operations described herein that form part of the above-described systems and methods are useful machine operations. The presently disclosed systems relate to devices or apparatuses for performing these operations. The devices or apparatuses can be specially constructed for the required purpose, or can be a general-purpose computer selectively activated or configured by a computer program stored in the computer. In particular, various general-purpose machines can be used with computer programs written in accordance with the teachings herein, or it may be more convenient to construct a specialized apparatus to perform the required operations.

The above-described systems and methods can be embodied at least in part as computer readable code on a computer readable medium. The computer readable medium is any data storage device that can store data, which can thereafter be read by a computer system. Examples of a computer readable medium include hard drives, read-only memory, random-access memory, CD-ROMs, CD-Rs, CD-RWs, magnetic tapes, and other optical and non-optical data storage devices. The computer readable medium can also be distributed over a network-coupled computer system so that the computer readable code is stored and executed in a distributed fashion.

It will be appreciated by those skilled in the art that modifications to and variations of the above-described systems and methods may be made without departing from the inventive concepts disclosed herein. Accordingly, the disclosure should not be viewed as limited except as by the scope and spirit of the appended claims.