CN101924943A - Real-time low-bit rate video transcoding method based on H.264 - Google Patents
Real-time low-bit rate video transcoding method based on H.264 Download PDFInfo
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Abstract
The invention relates to a real-time low-bit rate video transcoding method based on H.264, belonging to the field of multi-media signal processing and mainly solving the problem that the transcoding speed is demanded to be high at low bit rate. Video transcoding in the invention is divided into an off-line modeling stage and an online transcoding stage; a model identification technology is utilized to select a macrolbock prediction model; the macrolbock prediction model selection process is a category decision-making process in model identification; and extracted characteristics are input into a classifier to obtain a classification result, i.e. a macroblock prediction model. The classifier is obtained through offline training. The method not only can remarkably accelerate the transcoding speed but also ensures the quality of a recoded image to meet the demand of real-time performance. The invention is suitable for the fields of wireless video monitoring, internet video monitoring and wireless video on demand.
Description
Technical field
The invention belongs to field of multimedia signal processing, particularly a kind of real-time low-bit rate video transcoding method based on H.264 is mainly used in wireless video on-demand, fields such as long-range (wireless) video monitoring.
Background technology
The applied environment of video is very complicated, and from channel transmitted, storage medium all has nothing in common with each other to playback terminal etc.In these are used, usually need the image size in the video flowing, frame per second, each parameter such as picture quality is adjusted, thereby meets the requirement of access network and playback terminal.Such as in the described video request program of Fig. 1, just can on video server, add the video code conversion module, the user just can finish program request by wireless terminal, thereby has solved the narrow problem that can't program request of wireless channel.In video monitoring, real-time scene video stream has reduced bit rate through after the transcoding, just can carry out remote monitoring by the Internet or mobile intelligent terminal, thereby no longer be confined in the Control Room.
The input of video code conversion is a kind of bitstream format (spatial resolution S1, temporal resolution T1, code check R1, standard C 1 etc.), through the transcoding module, can obtain another output bit flow form (spatial resolution S2, temporal resolution T2, code check R2, standard C 2 etc.).The basic process of video code conversion is seen Fig. 2.
According to the input and output bitstream format, video code conversion is divided in standard room transcoding and the standard two kinds of transcodings usually.The standard room transcoding is meant that incoming bit stream and output bit flow belong to different standards.Transcoding refers to that the input and output bit stream belongs to same standard in the standard, at this moment the purpose of transcoding mainly is to reduce bit rate output, thereby adapt to different bandwidth, often be divided into spatial resolution transcoding (picture size) again, temporal resolution transcoding (frame per second), three aspects of bit rate transcoding (picture quality).
H.264 the standard developed jointly of the JVT that is made up of ITU-T and ISO/IEC of standard is also referred to as MPEG-4AVC.H.264 for macro block provides multiple predictive mode available, thereby improved compression performance.Because each macro block finally can only use a kind of predictive mode, this just needs the alternative various modes of traversal, finally selects the best predictive mode of compression performance.Therefore, one of topmost part consuming time in the H.264 transcoder that the selection of macroblock prediction pattern is based on, it has had a strong impact on transcoding speed, makes transcoder be very limited under the occasion of high real-time requiring.Therefore, in based on video code conversion H.264, how selecting the predictive mode of macro block fast, is one of most critical factor of decision transcoding speed.
Summary of the invention
In order to solve under the low bit rate and to require the fireballing problem of transcoding, the purpose of this invention is to provide a kind of real-time low-bit rate video transcoding method based on H.264, this method is based on the video code conversion under H.264 the low bit rate, emphasis is the system of selection of macroblock prediction pattern, be applicable to wireless video monitoring, the internet video monitoring, fields such as wireless video on-demand, this method not only can significantly be accelerated transcoding speed, and guaranteed the re-encoded picture quality, satisfied the requirement of real-time.
The present invention solves the technical scheme that its technical problem takes:
The present invention utilizes mode identification technology to carry out the selection of macroblock prediction pattern.The process of macroblock prediction model selection is exactly the process of classification decision-making in the pattern recognition.The feature of extracting is input in the grader, obtains classification results, just the predictive mode of macro block.Grader obtains by off-line training, therefore, the video code conversion of the present invention design is divided into off-line modeling and two stages of online transcoding are carried out off-line modeling: be used for finishing the design of grader, during online transcoding, use this grader to finish selection to the macroblock prediction pattern;
The step of off-line modeling comprises:
1) select video: different video sequences possesses different features, at first needs to pick out the video sequence that possesses various characteristic features commonly used, and uses H.264 standard to encode these video sequences;
2) video decode: use H.264 decoder, the sample video sequence that compression is good carries out complete decoding, obtains the pixel domain data;
3) extract feature: the extracting data that obtains from decoding goes out predictive mode, residual error data, three features of quantization parameter; Use the syntype search method to obtain optimization model when first three characteristics determined simultaneously as the target classification;
4) classifier design:, use existing ripe mode identification method to design grader with aforementioned three features and target classification.
Online transcoding: the grader that uses off-line modeling to obtain, according to feature, finish classification feature at line drawing, promptly obtain the model prediction of macro block; The step of online transcoding comprises:
1) line decode: use H.264 decoder, the live video stream that complete decoding is online obtains the pixel domain data, and the coding/decoding method here is identical with the coding/decoding method in off-line modeling stage;
2) extract feature: from line decode information, extract predictive mode, residual error data, three features of quantization parameter; Extracting the method for these three features and off-line modeling, to extract the method for three features identical;
3) model prediction: with three features of aforementioned extraction, be input in the grader of off-line foundation, obtain classification results, just predictive mode is finished the selection of macro block mode prediction;
4) recomputate motion vector: the macroblock prediction pattern at selecting, recomputate motion vector;
5) recompile: use the predictive mode choose and the motion vector that recomputates, again video is encoded and export.
Off-line modeling and online transcoding all need to extract three features from decoded information: predictive mode, residual error data, quantization parameter.Three feature extracting method steps are as follows described in the present invention:
1) predictive mode: the image zoom factor in the spatial resolution transcoding of support of the present invention is 2, and therefore macro block correspondence to be encoded 4 macro blocks in the encoded image, and each macro block all has a predictive mode, as shown in Figure 1.In order to reduce the dimension of characteristic vector as far as possible, improve transcoding speed, the calculated value of predictive mode feature is the predictive mode sum of these 4 macro blocks among the present invention;
2) residual error data: what adopt H.264 is the integer transform of 4x4, just a macro block has comprised 16 4x4 pieces, and each 4x4 piece all has nonzero coefficient separately, this data description the character of current 4x4 piece, these character comprise: whether texture is abundant, and whether move violent; The calculated value of this feature is the ratio of the nonzero coefficient in 4 all 4x4 pieces that macro block comprised in the original image among the present invention;
3) quantization parameter: the bit rate transcoding utilizes re-quantization to realize in the present invention; The calculated value of quantization parameter feature is that the output quantization parameter deducts the input quantization parameter.
In the model prediction of online transcoding,, then 8x8 piece is not continued classification if prediction is the P8x8 type.
In the online transcoding step 4),, adopt the median method to recomputate the motion vector of macro block in spatial resolution transcoding part.
In the off-line modeling step 4), ripe mode identification method comprises: SVMs or artificial neural net.
The invention has the beneficial effects as follows:
1), made full use of the abundant information of source code flow, carry out the macroblock prediction model selection fast, and guarantee the correctness selected as far as possible; Extracted residual error data from decoded information, macro block (mb) type, quantization parameter etc. are as feature, and these features are all closely bound up with the block type of coding side.Do not extract motion vector feature more consuming time, guaranteed that like this extraction feature is consuming time less, thereby transcoding speed is very fast.
2), based on the video code conversion of pixel domain, do not have any drift effect.The present invention has adopted the video code conversion of pixel domain, drift error can not occur, also just can not cause drift effect, thereby guarantee the re-encoded picture quality.
3), in the selection of macroblock prediction pattern of the present invention, used the off-line model that trains, in the online transcoding, only be the process of classification decision-making, so transcoding speed is fast, computation complexity is low, thereby has satisfied the requirement of real-time.
4), can satisfy the spatial resolution transcoding simultaneously, the transcoding that temporal resolution transcoding and bit rate transcoding are three types.And can be provided with according to the user, select which kind of transcoding, perhaps any two kinds of transcodings can combination in any.
5), in the off-line training, selected the video sequence that possesses each category feature for use, so this invention is applicable to various video type, comprises that motion is violent, and motion is mild, texture-rich, and texture is simple etc.
6), more effective under real-time low bit rate.The feature that the method for the present invention's design is extracted is less, and computational complexity is low.In addition, at the actual conditions of low bit rate, under the prerequisite that does not influence compression performance, special processing the selection of macro block (mb) type P8x8, thereby further accelerated transcoding speed.
Description of drawings
The existing application principle figure of Fig. 1 transcoding in video-on-demand service;
Fig. 2 video code conversion schematic diagram;
Fig. 3 code-transferring method flow chart of the present invention;
Macro block in Fig. 4 spatial resolution transcoding.
Embodiment
For the ease of understanding and implementing the present invention, come the present invention is described in further detail below in conjunction with the wireless video on-demand example.
As Fig. 3, in wireless video on-demand, the video flowing of having encoded leaves on the video server, and these video flowings all are that promptly picture size is big in the prerequisite lower compression of high bit rate, the frame per second height, and picture quality is better.Carry out certain video-frequency band of program request as wireless terminal user, corresponding desired parameter can be sent to video server simultaneously, these parameters comprise: picture size, frame per second, bit rate etc.Video server starts the transcoding module according to the requirement of these parameters, the video stream transcoding that has encoded is arrived under the desired form, and in real time the video flowing behind the transcoding is sent to user terminal.
The transcoding module can use the grader that designs in advance when the real-time online transcoding.This grader need be trained under off-line state and be obtained, so the present invention just divides for off-line modeling and two stages of online transcoding.
Off-line modeling is used for finishing the design of grader, during online transcoding, uses this grader to finish selection to the macroblock prediction pattern.The step that the off-line modeling stage implements is as follows:
1) selects video.Be generally the natural video frequency sequence in the video request program, thereby have various features, whether violent as motion, whether exist camera lens to switch, whether texture is abundant etc.Need to pick out the video sequence that possesses various characteristic features commonly used in the enforcement, and use H.264 standard to encode these video sequences.Certainly, if certain video on-demand system is at specific application, Basketball Match for example, the type sequence of then selecting preferably also is the Basketball Match fragment.
2) video decode.Use H.264 decoder, the sample video sequence that compression is good carries out complete decoding, obtains the pixel domain data.Can guarantee like this in whole transcoding process, can not introduce drift error, thereby guarantee picture quality.Can independent development based on H.264 decoder, also can use the decoding software system that increases income commonly used, as ffmpeg.
3) extract feature.From decoded information, extract predictive mode, residual error data, three features of quantization parameter.The optimization model of using the syntype search method to obtain simultaneously to determine when first three feature is as the target classification.The syntype search method is exactly all available predictive modes of traversal, and selects the result of a compression performance optimum, and the implementation process of this searching method can be used for reference the H.264 open source software JM series that JVT recommends.
4) classifier design.At each macro block in the sequence, the processing through decoding and two steps of extraction feature can obtain three features and a target classification, and the training sample that this has just constituted a known class writes file with it by certain format.All video sequences of choosing are all carried out identical processing, obtain sample and write file.Start the classifier design module,, from feature and target classification place file, read sample data, be input in the training module and train, obtain final grader as the training module of SVMs.Certainly, also can obtain grader by the artificial neural network training module.The grader that trains can store in certain file, uses during in order to online transcoding.Also can directly write sorter model in the online transcoding module as the stationary source code.
Video server starts online transcoding module according to the parameter request of user side, and the grader that online transcoding uses off-line modeling to obtain according to the feature at line drawing, is finished classification feature.The concrete implementation step of this module is as follows:
1) line decode.Starting H.264, decoder obtains the pixel domain data with online live video stream complete decoding.The coding/decoding method here is identical with the off-line modeling stage.
2) extract feature.From decoded information, extract predictive mode, residual error data, three features of quantization parameter.The method of extracting these three features is identical with off-line modeling.
3) model prediction.With three features extracting, be input in the grader of off-line foundation, obtain classification results, just predictive mode is finished the selection of macro block mode prediction.Predictive mode P8x8 relatively is applicable under the high bit rate in H.264, the scene that motion is violent and details is abundant.Owing to also need to segment fritter under this pattern, so computational complexity height up to 4x4.In wireless video on-demand was used, bit rate was lower, and real-time has relatively high expectations, if therefore prediction be the P8x8 type, then 8x8 piece is not continued to decompose, thereby improves transcoding speed.
4) recomputate motion vector.Every kind of predictive mode, all corresponding one or more motion vectors, therefore selecting needs to recomputate motion vector after the predictive mode.In the spatial resolution transcoding, the present invention adopts the median method to recomputate the motion vector of macro block.Motion vector after recomputating needs further refinement, could accurately reflect the actual conditions of motion, and general refinement step-length is 2 pixels.
5) recompile.The predictive mode that use chooses, the motion vector that recomputates is encoded to video again and is exported.
All need to extract predictive mode, residual error data and three features of quantization parameter in off-line modeling and each stage of online transcoding from decoded information, these three Feature Extraction methods are as follows:
1) predictive mode: the image zoom factor in the spatial resolution transcoding is defined as 2, macro block correspondence to be encoded the macro block 1～macro block 4 in the encoded image, totally 4 macro blocks, each macro block all has a predictive mode, and the calculated value of predictive mode feature is the predictive mode sum of these 4 macro blocks in the described online transcoding; As Fig. 4.
2) residual error data: the calculated value of this feature is the ratio of the nonzero coefficient in 4 all 4x4 pieces that macro block comprised in the original image;
3) quantization parameter: the calculated value of this feature is that the output quantization parameter deducts the input quantization parameter.
Use method of the present invention that coding and decoding video sequence commonly used has been carried out real-time transcoding experiment.Than the syntype search method of H.264 reference software JM, under the very little prerequisite of compression performance loss, the 15-20 that transcoding speed of the present invention is JM doubly.Under low bit rate situations such as wireless video on-demand, the transcoding speed of this method is faster, is about 25 times of JM.
Claims (5)
1. a real-time low-bit rate video transcoding method based on H.264 is characterized in that, comprises two stages of off-line modeling and online transcoding;
In the described off-line modeling stage, comprise following steps:
1) selects video: at first need to pick out the video sequence that possesses various characteristic features commonly used, and use H.264 standard to encode these video sequences;
2) video decode: use H.264 decoder, the sample video sequence that compression is good carries out complete decoding, obtains the pixel domain data;
3) extract feature: the extracting data that obtains from decoding goes out predictive mode, residual error data, three features of quantization parameter; Use the syntype search method to obtain optimization model when first three characteristics determined simultaneously as the target classification;
4) classifier design:, use existing ripe mode identification method to design grader with aforementioned three features and target classification;
Described online transcoding comprises following steps:
1) line decode: use H.264 decoder, the live video stream that complete decoding is online obtains the pixel domain data, and the coding/decoding method here is identical with the coding/decoding method in off-line modeling stage;
2) extract feature: from line decode information, extract predictive mode, residual error data and three features of quantization parameter; The method of these three features of the extraction here is identical with the method that off-line modeling is extracted three features;
3) model prediction: with three features of aforementioned extraction, be input in the grader of off-line foundation, obtain classification results, just predictive mode is finished the selection of macro block mode prediction;
4) recomputate motion vector: the macroblock prediction pattern at selecting, recomputate motion vector;
5) recompile: use the predictive mode choose and the motion vector that recomputates, again video is encoded and export.
2. a kind of real-time low-bit rate video transcoding method based on H.264 according to claim 1, it is characterized in that, off-line modeling and online transcoding all need to extract predictive mode, residual error data and three features of quantization parameter from decoded information, these three Feature Extraction methods are as follows:
1) predictive mode: the image zoom factor in the spatial resolution transcoding is defined as 2, macro block correspondence to be encoded 4 macro blocks in the encoded image, each macro block all has a predictive mode, and the calculated value of predictive mode feature is the predictive mode sum of these 4 macro blocks in the described online transcoding;
2) residual error data: the calculated value of this feature is the ratio of the nonzero coefficient in 4 all 4x4 pieces that macro block comprised in the original image;
3) quantization parameter: the calculated value of this feature is that the output quantization parameter deducts the input quantization parameter.
3. a kind of real-time low-bit rate video transcoding method based on H.264 according to claim 1 is characterized in that, in the model prediction of online transcoding, if prediction is the P8x8 type, then 8x8 piece is not continued classification.
4. a kind of real-time low-bit rate video transcoding method based on H.264 according to claim 1 and 2 is characterized in that in the online transcoding step 4), in the spatial resolution transcoding, the calculating of motion vector is to be finished by the median method.
5. a kind of real-time low-bit rate video transcoding method based on H.264 according to claim 1 is characterized in that in the off-line modeling step 4), ripe mode identification method comprises: SVMs or artificial neural net.
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