US20140254659A1 - Video coding method using at least evaluated visual quality and related video coding apparatus - Google Patents

Video coding method using at least evaluated visual quality and related video coding apparatus Download PDF

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US20140254659A1
US20140254659A1 US14/184,688 US201414184688A US2014254659A1 US 20140254659 A1 US20140254659 A1 US 20140254659A1 US 201414184688 A US201414184688 A US 201414184688A US 2014254659 A1 US2014254659 A1 US 2014254659A1
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visual quality
coding
video coding
unit
deciding
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Cheng-Tsai Ho
Chi-cheng Ju
Ding-Yun Chen
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MediaTek Inc
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MediaTek Inc
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Assigned to MEDIATEK INC. reassignment MEDIATEK INC. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: CHEN, DING-YUN, HO, CHENG-TSAI, JU, CHI-CHENG
Priority to PCT/CN2014/073146 priority patent/WO2014139387A1/fr
Priority to CN201480014165.2A priority patent/CN105075255A/zh
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    • H04N19/00024—
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/80—Details of filtering operations specially adapted for video compression, e.g. for pixel interpolation
    • H04N19/82—Details of filtering operations specially adapted for video compression, e.g. for pixel interpolation involving filtering within a prediction loop
    • H04N19/00151—
    • H04N19/00296—
    • H04N19/00369—
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/102—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or selection affected or controlled by the adaptive coding
    • H04N19/117—Filters, e.g. for pre-processing or post-processing
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/134—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or criterion affecting or controlling the adaptive coding
    • H04N19/154—Measured or subjectively estimated visual quality after decoding, e.g. measurement of distortion
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/189—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the adaptation method, adaptation tool or adaptation type used for the adaptive coding
    • H04N19/196—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the adaptation method, adaptation tool or adaptation type used for the adaptive coding being specially adapted for the computation of encoding parameters, e.g. by averaging previously computed encoding parameters
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/169—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding
    • H04N19/18—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being a set of transform coefficients
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/46—Embedding additional information in the video signal during the compression process

Definitions

  • the disclosed embodiments of the present invention relate to video coding, and more particularly, to a video coding method using at least evaluated visual quality determined by one or more visual quality metrics and a related video coding apparatus.
  • the conventional video coding standards generally adopt a block based (or coding unit based) coding technique to exploit spatial redundancy.
  • the basic approach is to divide the whole source frame into a plurality of blocks (coding units), perform prediction on each block (coding unit), transform residues of each block (coding unit) using discrete cosine transform, and perform quantization and entropy encoding.
  • a reconstructed frame is generated in a coding loop to provide reference pixel data used for coding following blocks (coding units).
  • in-loop filter(s) may be used for enhancing the image quality of the reconstructed frame.
  • a de-blocking filter is included in an H.264 coding loop
  • a de-blocking filter and a sample adaptive offset (SAO) filter are included in an HEVC (High Efficiency Video Coding) coding loop.
  • the coding loop is composed of a plurality of processing stages, including transform, quantization, intra/inter prediction, etc.
  • one processing stage selects a video coding mode based on pixel-based distortion value derived from a source frame (i.e., an input frame to be encoded) and a reference frame (i.e., a reconstructed frame generated during the coding procedure).
  • the pixel-based distortion value may be a sum of absolute differences (SAD), a sum of transformed differences (SATD), or a sum of square differences (SSD).
  • the pixel-based distortion value merely considers pixel value differences between pixels of the source frame and the reference frame, and sometimes is not correlated to the actual visual quality of a reconstructed frame generated from decoding an encoded frame.
  • different processed images each derived from an original image and having the same pixel-based distortion (e.g., the same mean square error (MSE)) with respect to the original image, may present different visual quality to a viewer. That is, the smaller pixel-based distortion does not mean better visual quality in the human visual system.
  • MSE mean square error
  • a video coding method using at least evaluated visual quality obtained by one or more visual quality metrics and a related video coding apparatus are proposed.
  • an exemplary video coding method includes at least the following steps: utilizing a visual quality evaluation module for evaluating visual quality based on data involved in a coding loop; and referring to at least the evaluated visual quality for deciding a target configuration of at least one of a coding unit, a transform unit and a prediction unit.
  • another exemplary video coding method includes at least the following steps: utilizing a visual quality evaluation module for evaluating visual quality based on data involved in a coding loop; and referring to at least the evaluated visual quality for deciding a target coding parameter associated with at least one of a coding unit, a transform unit and a prediction unit in video coding.
  • an exemplary video coding apparatus includes a visual quality evaluation module and a coding circuit.
  • the visual quality evaluation module is arranged to evaluate visual quality based on data involved in a coding loop.
  • the coding circuit has the coding loop included therein, and is arranged to refer to at least the evaluated visual quality for deciding a target configuration of at least one of a coding unit, a transform unit and a prediction unit.
  • the another exemplary video coding apparatus includes a visual quality evaluation module and a coding circuit.
  • the visual quality evaluation module is arranged to evaluate visual quality based on data involved in a coding loop.
  • the coding circuit has the coding loop included therein, and is arranged to refer to at least the evaluated visual quality for deciding a target coding parameter associated with at least one of a coding unit, a transform unit and a prediction unit in video coding.
  • FIG. 1 is a block diagram illustrating a video coding apparatus according to an embodiment of the present invention.
  • FIG. 2 is a diagram illustrating inter modes for a coding unit to be configured by the video coding apparatus shown in FIG. 1 .
  • FIG. 3 is a diagram illustrating intra 4 ⁇ 4 modes for a coding unit to be configured by the video coding apparatus shown in FIG. 1 .
  • FIG. 4 is a diagram illustrating intra 16 ⁇ 16 modes for a coding unit to be configured by the video coding apparatus shown in FIG. 1 .
  • FIG. 5 is a diagram illustrating partition modes for a prediction unit to be configured by the video coding apparatus shown in FIG. 1 .
  • FIG. 6 is a diagram illustrating partition modes for a transform unit to be configured by the video coding apparatus shown in FIG. 1 .
  • FIG. 7 is a flowchart illustrating a video coding method according to a first embodiment of the present invention.
  • FIG. 8 is a flowchart illustrating a video coding method according to a second embodiment of the present invention.
  • the concept of the present invention is to incorporate characteristics of a human visual system into a video coding procedure to improve the video compression efficiency or visual quality. More specifically, visual quality evaluation is involved in the video coding procedure such that a reconstructed frame generated from decoding an encoded frame is capable of having enhanced visual quality. Further details of the proposed visual quality based video coding design are described as below.
  • FIG. 1 is a block diagram illustrating a video coding apparatus according to an embodiment of the present invention.
  • the video coding apparatus 100 is used to encode a source frame IMG IN to generate a bitstream BS carrying encoded frame information corresponding to the source frame IMG IN .
  • the video coding apparatus 100 includes a coding circuit 102 and a visual quality evaluation module 104 .
  • the architecture of the coding circuit 102 may be configured based on any conventional video encoding architecture.
  • the coding circuit 102 may follow the conventional video encoding architecture to have a plurality of processing stages implemented therein; however, this by no means implies that each of the processing stages included in the coding circuit 102 must be implemented using a conventional design.
  • any of the processing stages that is associated with the visual quality evaluation performed by the visual quality evaluation module 104 and/or is affected/controlled by the visual quality obtained by the visual quality evaluation module 104 still falls within the scope of the present invention.
  • the coding circuit 102 includes a coding loop composed of a splitting module 111 , a subtractor (i.e., an adder configured to perform a subtraction operation) 112 , a transform module 113 , a quantization module 114 , an inverse quantization module 116 , an inverse transform module 117 , an adder 118 , a de-blocking filter 119 , a sample adaptive offset (SAO) filter 120 , a frame buffer 121 , an inter prediction module 122 , and an intra prediction module 123 , where the inter prediction module 122 includes a motion estimation unit 124 and a motion compensation unit 125 .
  • a subtractor i.e., an adder configured to perform a subtraction operation
  • a transform module 113 e.e., a transform module 113 , a quantization module 114 , an inverse quantization module 116 , an inverse transform module 117 , an adder 118 , a de-blocking filter 119
  • the coding circuit 102 further includes an entropy coding module 115 arranged to generate the bitstream BS by performing entropy encoding upon quantized coefficients generated from the quantization module 114 .
  • an entropy coding module 115 arranged to generate the bitstream BS by performing entropy encoding upon quantized coefficients generated from the quantization module 114 .
  • the de-blocking filter 119 and the SAO filter 120 may be omitted/bypassed for certain applications. That is, the de-blocking filter 119 and/or the SAO filter 120 may be optional, depending upon actual design requirement.
  • fundamental operations of the processing stages included in the coding circuit 102 further description is omitted here for brevity. Concerning one or more of the processing stages that are affected/controlled by the visual quality determined by the visual quality evaluation module 104 , further description will be given as below.
  • the key feature of the present invention is using the visual quality evaluation module 104 for evaluating visual quality based on data involved in the coding loop of the coding circuit 102 .
  • the data involved in the coding loop and processed by the visual quality evaluation module 104 may be raw data of the source frame IMG IN .
  • the data involved in the coding loop and processed by the visual quality evaluation module 104 may be processed data derived from raw data of the source frame IMG IN .
  • the processed data used for evaluate the visual quality may be transformed coefficients generated by the transform module 113 , quantized coefficients generated by the quantization module 114 , reconstructed pixel data before the optional de-blocking filter 119 , reconstructed pixel data after the optional de-blocking filter 119 , reconstructed pixel data before the optional SAO filter 120 , reconstructed pixel data after the optional SAO filter 120 , reconstructed pixel data stored in the frame buffer 121 , motion-compensated pixel data generated by the motion compensation unit 125 , or intra-predicted pixel data generated by the intra prediction module 123 .
  • the visual quality evaluation performed by the visual quality evaluation module 104 may calculate one or more visual quality metrics to decide the evaluated visual quality.
  • the evaluated visual quality is derived from checking at least one image characteristic that affects human visual perception, and the at least one image characteristic may include sharpness, noise, blur, edge, dynamic range, blocking artifact, mean intensity (e.g., brightness/luminance), color temperature, scene composition (e.g., landscape, portrait, night scene, etc.), human face, animal presence, image content that attracts more or less interest (e.g., region of interest (ROI)), spatial masking (i.e., human's visual insensitivity of more complex texture), temporal masking (i.e., human's visual insensitivity of high-speed moving object), or frequency masking (i.e., human's visual insensitivity of higher pixel value variation).
  • ROI region of interest
  • the noise metric may be obtained by calculating other visual noise metric, such as an S-CIELAB metric, a vSNR (visual signal-to-noise ratio) metric, or a Keelan NPS (noise power spectrum) based metric.
  • the sharpness/blur metric may be obtained by measuring edge widths.
  • the edge metric may be a ringing metric obtained by measuring ripples or oscillations around edges.
  • the visual quality evaluation module 104 calculates a single visual quality metric (e.g., one of the aforementioned visual quality metrics) according to the data involved in the coding loop of the coding circuit 102 , and determines each evaluated visual quality solely based on the single visual quality metric. In other words, each evaluated visual quality may be obtained by referring to a single visual quality metric only.
  • a single visual quality metric e.g., one of the aforementioned visual quality metrics
  • the visual quality evaluation module 104 calculates a plurality of distinct visual quality metrics (e.g., many of the aforementioned visual quality metrics) according to the data involved in the coding loop of the coding circuit 102 , and determines each evaluated visual quality based on the distinct visual quality metrics.
  • each evaluated visual quality may be obtained by referring to a composition of multiple visual quality metrics.
  • the visual quality evaluation module 104 maybe configured to assign a plurality of pre-defined weighting factors to multiple visual quality metrics (e.g., a noise metric and a sharpness metric), and decide one evaluated visual quality by a weighted sum derived from the weighting factors and the visual quality metrics.
  • the visual quality evaluation module 104 may employ a Minkowski equation to determine a plurality of non-linear weighting factors for the distinct visual quality metrics, respectively; and then determine one evaluated visual quality by combining the distinct visual quality metrics according to respective non-linear weighting factors.
  • the evaluated visual quality ⁇ Q m is calculated using following equation:
  • ⁇ Q i is derived from each of the distinct visual quality metrics, and 16.9 is a single universal parameter based on psychophysical experiments.
  • the visual quality evaluation module 104 may employ a training-based manner (e.g., a support vector machine (SVM)) to determine a plurality of trained weighting factors for the distinct visual quality metrics, respectively; and then determines one evaluated visual quality by combining the distinct visual quality metrics according to respective trained weighting factors.
  • supervised learning models with associated learning algorithms are employed to analyze the distinct visual quality metrics and recognized patterns, and accordingly determine the trained weighting factors.
  • the evaluated visual quality is generated by the visual quality evaluation module 104 , the evaluated visual quality is referenced by the coding circuit 102 to control/configure one or more of the processing stages within the coding circuit 102 .
  • the source frame IMG IN is encoded based on characteristics of the human visual system to thereby allow a decoded/reconstructed frame to have enhanced visual quality.
  • the coding circuit 102 may be arranged to refer to the evaluated visual quality decided by the visual quality evaluation 104 for deciding a target configuration of at least one of a coding unit at the splitting module 111 , a transform unit at the transform module 113 and a prediction unit at the intra prediction module 123 /inter prediction module 122 , where the evaluated visual quality in this case may provide visual quality information for candidate video coding modes.
  • both of the evaluated visual quality (which is generated based on data involved in the coding loop) and the pixel-based distortion (which is generated based on at least a portion of raw data of the source frame IMG IN and at least a portion of processed data derived from the raw data of the source frame IMG IN ) are used to decide the target configuration of at least one of the coding unit at the splitting module 111 , the transform unit at the transform module 113 and the prediction unit at the intra prediction module 123 /inter prediction module 122 , where the evaluated visual quality in this case may provide visual quality information for candidate video coding modes, and the pixel-based distortion in this case may provide distortion information for candidate video coding mode modes. Further details are described as below.
  • the evaluated visual quality (each is determined based on a single visual quality metric or a composition of multiple visual quality metrics) may be referenced to decide a best video coding mode for a coding unit.
  • the splitting module 111 may refer to evaluated visual quality determined by the visual quality evaluation module 104 for each candidate inter mode and each candidate intra mode to decide which one of candidate inter modes and candidate intra modes should be selected for a coding unit.
  • the inter modes include four MB-modes and four 8 ⁇ 8-modes as shown in FIG. 2
  • the intra modes include nine 4 ⁇ 4 modes as shown in FIG. 3 and four 16 ⁇ 16 modes as shown in FIG.
  • the conventional video coding design calculates pixel-based distortion value Distortion (C, R) for each candidate mode, where C represent pixels in a current source frame, R represent pixels in a reconstructed frame, and the distortion value Distortion (C, R) may be an SAD value, an SATD value or an SSD value.
  • the conventional video coding design finds a best inter mode (e.g.,
  • the present invention proposes using the evaluated visual quality VQ(C or R′) derived from data involved in the coding loop of the coding unit 102 to find the best video coding mode for a coding unit, where each evaluated visual quality VQ(C or R′) for each candidate mode may be obtained by a single visual quality metric or a composition of multiple visual quality metrics, C represents raw data of the source frame IMG IN , and R′ represents processed data derived from raw data of the source frame IMG IN .
  • the splitting module 111 finds a best inter mode (e.g.,
  • the operation of referring to the evaluated visual quality for deciding the target configuration of the coding unit at the splitting module 111 may include: deciding a best mode from different intra modes of the coding unit; deciding a best mode from different inter modes of the coding unit; and/or deciding that the coding unit is an intra-mode coding unit or an inter-mode coding unit.
  • both of the evaluated visual quality e.g., VQ(C or R′)
  • the pixel-based distortion e.g., Distortion (C, R)
  • the splitting module 111 refers to the evaluated visual quality and the calculated pixel-based distortion to find a best inter mode (e.g., one of
  • a best intra mode e.g., min (best intra 4 ⁇ 4 mode, best intra 16 ⁇ 16 mode)
  • a best video coding mode for the coding unit e.g., min (best intra mode, best inter mode)
  • the splitting module 111 performs a coarse decision according to one of the evaluated visual quality and the calculated pixel-based distortion to determine a plurality of coarse configuration settings for a coding unit, and performs a fine decision according to another of the evaluated visual quality and the pixel-based distortion to determine at least one fine configuration setting from the coarse configuration settings, wherein the target configuration for the coding unit is derived from the at least one fine configuration setting.
  • the evaluated visual quality may be used to find M coarse configuration settings for a coding unit, such as some inter modes, some intra 4 ⁇ 4 modes and/or some intra 16 ⁇ 16 modes, from all possible N candidate configuration settings, and then the pixel-based distortion may be used to selected P fine configuration settings for the coding unit (N>M & M>P ⁇ 1) from inter modes, intra 4 ⁇ 4 modes and/or intra 16 ⁇ 16 modes selected based on the evaluated visual quality.
  • P a best video coding mode for the coding unit is determined by the fine decision based on the pixel-based distortion.
  • the pixel-based distortion may be used to find M coarse configuration settings for a coding unit, such as some inter modes, some intra 4 ⁇ 4 modes and/or some intra 16 ⁇ 16 modes, from all possible N candidate configuration settings, and then the evaluated visual quality may be used to selected P fine configuration settings for the coding unit (N>M & M>P ⁇ 1) from inter modes, intra 4 ⁇ 4 modes and/or intra 16 ⁇ 16 modes selected based on the pixel-based distortion.
  • P a best video coding mode for the coding unit is determined by the fine decision based on the evaluated visual quality.
  • the evaluated visual quality (each is determined based on a single visual quality metric or a composition of multiple visual quality metrics) may be referenced to decide a best video coding mode for a prediction unit.
  • the intra prediction include two partition modes for the prediction unit, and the inter prediction includes eight partition modes for the prediction unit, as shown in FIG. 5 .
  • the conventional video coding design calculates a distortion value Distortion (C, R) for each candidate mode, where C represent pixels in a current source frame, R represent pixels in a reconstructed frame, and the distortion value Distortion (C, R) may be an SAD value, an SATD value or an SSD value.
  • the conventional video coding design decides the configuration of a prediction unit by finding a best video coding mode with a smallest distortion value among distortion values of the candidate modes.
  • the present invention proposes using the evaluated visual quality VQ (C or R′) derived from data involved in the coding loop of the coding unit 102 to find a best video coding mode for a prediction unit, where each evaluated visual quality VQ(C or R′) may be a single visual quality metric or a composition of multiple visual quality metrics, C represents raw data of the source frame IMG IN , and R′ represents processed data derived from raw data of the source frame IMG IN .
  • the operation of referring to the evaluated visual quality for deciding the target configuration of the prediction unit at the intra prediction module 123 /inter prediction module 122 may include: deciding a size of the prediction unit; and/or deciding that the prediction unit is a symmetric prediction unit or an asymmetric prediction unit.
  • both of the evaluated visual quality (e.g., VQ(C or R′)) and the pixel-based distortion (e.g., Distortion (C, R)) may be involved in deciding the best video coding mode for a prediction unit.
  • the intra prediction module 123 /inter prediction module 122 refers to the evaluated visual quality to find a first video coding mode with best visual quality, refers to the calculated pixel-based distortion to find a second video coding mode with smallest distortion, and selects one of the first video coding mode and the second video coding mode as the best video coding mode for the prediction unit.
  • the intra prediction module 123 /inter prediction module 122 performs a coarse decision according to one of the evaluated visual quality and the pixel-based distortion to determine a plurality of coarse configuration settings for a prediction unit, and performs a fine decision according to another of the evaluated visual quality and the pixel-based distortion to determine at least one fine configuration setting from the coarse configuration settings, wherein the target configuration for the prediction unit is derived from the at least one fine configuration setting.
  • the evaluated visual quality (each is determined based on a single visual quality metric or a composition of multiple visual quality metrics) maybe referenced to decide a best video coding mode for a transform unit.
  • the transform unit includes several partition modes as shown in FIG. 6 .
  • the conventional video coding design calculates a distortion value Distortion (C, R) for each candidate mode, where C represent pixels in a current source frame, R represent pixels in a reconstructed frame, and the distortion value Distortion (C, R) may be an SAD value, an SATD value or an SSD value.
  • the conventional video coding design decides the configuration of a transform unit by finding a best mode with a smallest distortion value among distortion values of the candidate modes.
  • the present invention proposes using the evaluated visual quality VQ(C or R′) derived from data involved in the coding loop of the coding unit 102 to find a best video coding mode for a transform unit, where each evaluated visual quality VQ(C or R′) may be a single visual quality metric or a composition of multiple visual quality metrics, C represents raw data of the source frame IMG IN , and R′ represents processed data derived from raw data of the source frame IMG IN .
  • the operation of referring to the evaluated visual quality for deciding the target configuration of the transform unit at the transform module 113 may include: deciding a size of the transform unit; deciding a quad-tree depth of the transform unit; and/or deciding that the transform unit is a residual quad-tree (RQT) transform unit or a non-square quad-tree (NSQT) transform unit.
  • RQT residual quad-tree
  • NQT non-square quad-tree
  • both of the evaluated visual quality (e.g., VQ(C or R′)) and the pixel-based distortion (e.g., Distortion (C, R)) may be involved in deciding a best video coding mode for a transform unit.
  • the transform module 113 refers to the evaluated visual quality to find a first video coding mode with best visual quality, refers to the calculated pixel-based distortion to find a second video coding mode with smallest distortion, and selects one of the first video coding mode and the second video coding mode as the best video coding mode for the transform unit.
  • the transform module 113 performs a coarse decision according to one of the evaluated visual quality and the pixel-based distortion to determine a plurality of coarse configuration settings for a transform unit, and performs a fine decision according to another of the evaluated visual quality and the pixel-based distortion to determine at least one fine configuration setting from the coarse configuration settings, wherein the target configuration for the prediction unit is derived from the at least one fine configuration setting.
  • FIG. 7 is a flowchart illustrating a video coding method according to a first embodiment of the present invention. Provided that the result is substantially the same, the steps are not required to be executed in the exact order shown in FIG. 7 .
  • the video coding method may be briefly summarized as below.
  • Step 700 Start.
  • Step 702 Evaluate visual quality based on data involved in a coding loop, wherein the data involved in the coding loop may be raw data of a source frame or processed data derived from the raw data of the source frame, and each evaluated visual quality may be obtained from a single visual quality metric or a composition of multiple visual quality metrics.
  • Step 704 Check if pixel-based distortion should be used for video coding mode decision. If yes, go to step 706 ; otherwise, go to step 710 .
  • Step 706 Calculate the pixel-based distortion based on at least a portion of raw data of the source frame and at least a portion of processed data derived from the raw data of the source frame.
  • Step 708 Refer to both of the evaluated visual quality and the calculated pixel-based distortion for deciding a target configuration of at least one of a coding unit, a transform unit and a prediction unit. Go to step 712 .
  • Step 710 Refer to the evaluated visual quality for deciding a target configuration of at least one of a coding unit, a transform unit and a prediction unit.
  • Step 712 End.
  • the evaluated visual quality determined by the visual quality evaluation module 104 can be used to determine a target configuration of at least one of a coding unit, a transform unit and a prediction unit.
  • the coding circuit 102 may be arranged to refer to the aforementioned visual quality determined by the visual quality evaluation module 104 for deciding target coding parameter(s) associated with at least one of a coding unit, a transform unit and a prediction unit in video coding, where the evaluated visual quality in this case may provide visual quality information for candidate video coding modes
  • the coding unit may be configured at the splitting module 111
  • the transform unit may be configured at the transform module 113
  • the prediction unit maybe configured at the intra prediction module 123 /inter prediction module 122 .
  • the target coding parameter(s) may include a quantization parameter (which is used by quantization module 114 and inverse quantization module 116 ) and/or a transform parameter (which is used by transform module 113 and inverse transform module 117 ).
  • the target coding parameter(s) set based on the evaluated visual quality may be included in the bitstream BS generated by encoding the source frame IMG IN . That is, the target coding parameter(s) can be transmitted to a video decoding apparatus to facilitate the decoder-side video processing operation.
  • the visual quality evaluation performed by the visual quality evaluation module 104 has been detailed above, further description directed to obtaining the evaluated visual quality based on one or more visual quality metrics is omitted here for brevity.
  • both of the evaluated visual quality (which is obtained based on data involved in the coding loop) and the pixel-based distortion (which is generated based on at least a portion of raw data of the source frame IMG IN and at least a portion of processed data derived from the raw data of the source frame IMG IN ) are used to decide target coding parameter(s) associated with at least one of a coding unit, a transform unit and a prediction unit in video coding, wherein the evaluated visual quality in this case may provide visual quality information for candidate video coding modes, and the calculated pixel-based distortion in this case may provide distortion information for candidate video coding modes.
  • the transform module 113 refers to the evaluated visual quality to decide a first transform parameter setting with best visual quality, refers to the calculated pixel-based distortion to decide a second transform parameter setting with smallest distortion, and selects one of the first transform parameter setting and the second transform parameter setting to set the transform parameter.
  • the quantization module 114 refers to the evaluated visual quality to decide a first quantization parameter setting with best visual quality, refers to the calculated pixel-based distortion to decide a second quantization parameter setting with smallest distortion, and selects one of the first quantization parameter setting and the second quantization parameter setting to set the quantization parameter.
  • the transform module 113 performs a coarse decision according to one of the evaluated visual quality and the pixel-based distortion to determine a plurality of coarse parameter settings for a transform parameter, and performs a fine decision according to another of the evaluated visual quality and the pixel-based distortion to determine at least one fine parameter setting from the coarse parameter settings, wherein a target coding parameter (i.e., the transform parameter) is derived from the at least one fine parameter setting.
  • a target coding parameter i.e., the transform parameter
  • the quantization module 114 performs a coarse decision according to one of the evaluated visual quality and the pixel-based distortion to determine a plurality of coarse parameter settings for a quantization parameter, and performs a fine decision according to another of the evaluated visual quality and the pixel-based distortion to determine at least one fine parameter setting from the coarse parameter settings, wherein a target coding parameter (i.e., the quantization parameter) is derived from the at least one fine parameter setting.
  • a target coding parameter i.e., the quantization parameter
  • FIG. 8 is a flowchart illustrating a video coding method according to a second embodiment of the present invention. Provided that the result is substantially the same, the steps are not required to be executed in the exact order shown in FIG. 8 .
  • the video coding method may be briefly summarized as below.
  • Step 800 Start.
  • Step 802 Evaluate visual quality based on data involved in a coding loop, wherein the data involved in the coding loop may be raw data of a source frame or processed data derived from the raw data of the source frame, and each evaluated visual quality may be obtained from a single visual quality metric or a composition of multiple visual quality metrics.
  • Step 804 Check if pixel-based distortion should be used for coding parameter decision. If yes, go to step 806 ; otherwise, go to step 810 .
  • Step 806 Calculate the pixel-based distortion based on at least a portion of raw data of the source frame and at least a portion of processed data derived from the raw data of the source frame.
  • Step 808 Refer to both of the evaluated visual quality and the calculated pixel-based distortion for deciding a target coding parameter (e.g., a quantization parameter or a transform parameter) associated with at least one of a coding unit, a transform unit and a prediction unit in video coding. Go to step 812 .
  • a target coding parameter e.g., a quantization parameter or a transform parameter
  • Step 810 Refer to the evaluated visual quality for deciding a target coding parameter (e.g., a quantization parameter or a transform parameter) associated with at least one of a coding unit, a transform unit and a prediction unit in video coding.
  • a target coding parameter e.g., a quantization parameter or a transform parameter
  • Step 812 End.

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  • Computing Systems (AREA)
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  • Compression Or Coding Systems Of Tv Signals (AREA)
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US10091500B2 (en) 2018-10-02
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