EP4639888A1

EP4639888A1 - Reference sample selection for cross-component intra prediction

Info

Publication number: EP4639888A1
Application number: EP23818002.0A
Authority: EP
Inventors: Philippe Bordes; Karam NASER; Thierry DUMAS; Franck Galpin
Original assignee: InterDigital CE Patent Holdings SAS
Current assignee: InterDigital CE Patent Holdings SAS
Priority date: 2022-12-19
Filing date: 2023-12-04
Publication date: 2025-10-29
Also published as: WO2024132468A1; JP2026500161A; CN120419165A; MX2025007169A

Abstract

The cross-component model generally describes a relation between the luma samples and associated chroma samples. Using the cross-component model parameters derived based on a neighboring region, the chroma samples of the current block can be predicted from the collated reconstructed luma samples. Because the cross-component model parameters are derived at both the encoder and decoder, these model parameters need not to be signaled explicitly and therefore can reduce signaling overhead. In one implementation, to make the derived model more accurate, the signal characteristics of the luma reference samples are selected to better match the signal characteristics of the reconstructed luma samples in the current block, using at least one of histogram of the neighboring samples, shape of the current block and intra mode direction. After the neighboring region is selected, the region can be further refined by using a set of region variants.

Description

REFERENCE SAMPLE SELECTION FOR CROSS-COMPONENT INTRA

PREDICTION

TECHNICAL FIELD

[1] The present embodiments generally relate to a method and an apparatus for crosscomponent intra prediction in video encoding and decoding.

BACKGROUND

[2] To achieve high compression efficiency, image and video coding schemes usually employ prediction and transform to leverage spatial and temporal redundancy in the video content. Generally, intra or inter prediction is used to exploit the intra or inter picture correlation, then the differences between the original block and the predicted block, often denoted as prediction errors or prediction residuals, are transformed, quantized, and entropy coded. To reconstruct the video, the compressed data are decoded by inverse processes corresponding to the entropy coding, quantization, transform, and prediction.

SUMMARY

[3] According to an embodiment, a method of video encoding is presented, comprising: obtaining reconstructed luma samples of a block of a picture; selecting, from a plurality of candidate neighboring regions for said block, one neighboring region for said block; obtaining reconstructed luma samples and reconstructed chroma samples of said selected neighboring region; obtaining model parameters of a cross-component model that models a relation between said reconstructed luma samples and reconstructed chroma samples of said selected neighboring region; predicting chroma samples of said block based on said reconstructed luma samples of said block and said cross-component model with said model parameters; and encoding said chroma samples of said block based on said predicted chroma samples.

[4] According to another embodiment, a method of video decoding is provided, comprising: obtaining reconstructed luma samples of a block of a picture; selecting, from a plurality of candidate neighboring regions for said block, one neighboring region for said block; obtaining reconstructed luma samples and reconstructed chroma samples of said selected neighboring region; obtaining model parameters of a cross-component model that models a relation between said reconstructed luma samples and reconstructed chroma samples of said selected neighboring region; predicting chroma samples of said block based on said reconstructed luma samples of said block and said cross-component model with said model parameters; and decoding said chroma samples of said block based on said predicted chroma samples.

[5] According to another embodiment, an apparatus for video encoding is provided, comprising at least one memory and one or more processors, wherein said one or more processors are configured to: obtain reconstructed luma samples of a block of a picture; select, from a plurality of candidate neighboring regions for said block, one neighboring region for said block; obtain reconstructed luma samples and reconstructed chroma samples of said selected neighboring region; obtain model parameters of a cross-component model that models a relation between said reconstructed luma samples and reconstructed chroma samples of said selected neighboring region; predict chroma samples of said block based on said reconstructed luma samples of said block and said cross-component model with said model parameters; and encode said chroma samples of said block based on said predicted chroma samples.

[6] An apparatus for video decoding is provided, comprising at least one memory and one or more processors, wherein said one or more processors are configured to: obtain reconstructed luma samples of a block of a picture; select, from a plurality of candidate neighboring regions for said block, one neighboring region for said block; obtain reconstructed luma samples and reconstructed chroma samples of said selected neighboring region; obtain model parameters of a crosscomponent model that models a relation between said reconstructed luma samples and reconstructed chroma samples of said selected neighboring region; predict chroma samples of said block based on said reconstructed luma samples of said block and said cross-component model with said model parameters; and decode said chroma samples of said block based on said predicted chroma samples.

[7] One or more embodiments also provide a computer program comprising instructions which when executed by one or more processors cause the one or more processors to perform the encoding method or decoding method according to any of the embodiments described herein. One or more of the present embodiments also provide a computer readable storage medium having stored thereon instructions for encoding or decoding a video according to the methods described herein. [8] One or more embodiments also provide a computer readable storage medium having stored thereon video data generated according to the methods described above. One or more embodiments also provide a method and apparatus for transmitting or receiving the video data generated according to the methods described herein.

BRIEF DESCRIPTION OF THE DRAWINGS

[9] FIG. 1 illustrates a block diagram of a system within which aspects of the present embodiments may be implemented.

[10] FIG. 2 illustrates a block diagram of an embodiment of a video encoder.

[11] FIG. 3 illustrates a block diagram of an embodiment of a video decoder.

[12] FIG. 4 illustrates the spatial part of the convolutional filter in CCLM.

[13] FIG. 5 illustrates an example of classifying the neighboring samples into two groups.

[14] FIG. 6 illustrates the spatial part of the convolutional filter in CCCM.

[15] FIG. 7 illustrates the reference area (with its paddings) used to derive the filter coefficients.

[16] FIG. 8 illustrates an overview of the process of the CC prediction methods.

[17] FIG. 9 illustrates the spatial samples used for GL-CCCM.

[18] FIG. 10 illustrates neighboring regions used to derive the CC-model.

[19] FIG. 11A illustrates an example of a derived CC linear model using {(L0, CO), (LI, Cl), (L3, C3)}, and FIG. 1 IB illustrates an example of a derived CC linear model using {(LI, Cl), (L3, C3)}.

[20] FIG. 12 illustrates an example of a histogram and a normalized cumulated histogram (i.e., cumulative distribution function), respectively.

[21] FIG. 13 illustrates a process of selecting the region used to derive the CC-model using histogram matching, according to an embodiment.

[22] FIG. 14 illustrates examples of refining a region selected from the primary list of regions, according to an embodiment.

[23] FIG. 15 illustrates examples of region variants derived from the primary list of three regions, according to an embodiment.

[24] FIG. 16 illustrates examples of samples used to derive two models, where the hatched samples are used by both models, according to an embodiment.

[25] FIG. 17 illustrates examples of reference region shapes adapted to the current PU shape.

DETAILED DESCRIPTION

[26] FIG. 1 illustrates a block diagram of an example of a system in which various aspects and embodiments can be implemented. System 100 may be embodied as a device including the various components described below and is configured to perform one or more of the aspects described in this application. Examples of such devices, include, but are not limited to, various electronic devices such as personal computers, laptop computers, smartphones, tablet computers, digital multimedia settop boxes, digital television receivers, personal video recording systems, connected home appliances, and servers. Elements of system 100, singly or in combination, may be embodied in a single integrated circuit, multiple ICs, and/or discrete components. For example, in at least one embodiment, the processing and encoder/decoder elements of system 100 are distributed across multiple ICs and/or discrete components. In various embodiments, the system 100 is communicatively coupled to other systems, or to other electronic devices, via, for example, a communications bus or through dedicated input and/or output ports. In various embodiments, the system 100 is configured to implement one or more of the aspects described in this application.

[27] The system 100 includes at least one processor 110 configured to execute instructions loaded therein for implementing, for example, the various aspects described in this application. Processor 110 may include embedded memory, input output interface, and various other circuitries as known in the art. The system 100 includes at least one memory 120 (e.g., a volatile memory device, and/or a non-volatile memory device). System 100 includes a storage device 140, which may include non-volatile memory and/or volatile memory, including, but not limited to, EEPROM, ROM, PROM, RAM, DRAM, SRAM, flash, magnetic disk drive, and/or optical disk drive. The storage device 140 may include an internal storage device, an attached storage device, and/or a network accessible storage device, as non-limiting examples.

[28] System 100 includes an encoder/decoder module 130 configured, for example, to process data to provide an encoded video or decoded video, and the encoder/decoder module 130 may include its own processor and memory. The encoder/decoder module 130 represents module(s) that may be included in a device to perform the encoding and/or decoding functions. As is known, a device may include one or both of the encoding and decoding modules. Additionally, encoder/decoder module 130 may be implemented as a separate element of system 100 or may be incorporated within processor 110 as a combination of hardware and software as known to those skilled in the art.

[29] Program code to be loaded onto processor 110 or encoder/decoder 130 to perform the various aspects described in this application may be stored in storage device 140 and subsequently loaded onto memory 120 for execution by processor 110. In accordance with various embodiments, one or more of processor 110, memory 120, storage device 140, and encoder/decoder module 130 may store one or more of various items during the performance of the processes described in this application. Such stored items may include, but are not limited to, the input video, the decoded video or portions of the decoded video, the bitstream, matrices, variables, and intermediate or final results from the processing of equations, formulas, operations, and operational logic.

[30] In several embodiments, memory inside of the processor 110 and/or the encoder/decoder module 130 is used to store instructions and to provide working memory for processing that is needed during encoding or decoding. In other embodiments, however, a memory external to the processing device (for example, the processing device may be either the processor 110 or the encoder/decoder module 130) is used for one or more of these functions. The external memory may be the memory 120 and/or the storage device 140, for example, a dynamic volatile memory and/or a non-volatile flash memory. In several embodiments, an external non-volatile flash memory is used to store the operating system of a television. In at least one embodiment, a fast external dynamic volatile memory such as a RAM is used as working memory for video coding and decoding operations, such as for MPEG-2, HEVC, or VVC (Versatile Video Coding).

[31] The input to the elements of system 100 may be provided through various input devices as indicated in block 105. Such input devices include, but are not limited to, (i) an RF portion that receives an RF signal transmitted, for example, over the air by a broadcaster, (ii) a Composite input terminal, (iii) a USB input terminal, and/or (iv) an HDMI input terminal.

[32] In various embodiments, the input devices of block 105 have associated respective input processing elements as known in the art. For example, the RF portion may be associated with elements suitable for (i) selecting a desired frequency (also referred to as selecting a signal, or band-limiting a signal to a band of frequencies), (ii) down converting the selected signal, (iii) bandlimiting again to a narrower band of frequencies to select (for example) a signal frequency band which may be referred to as a channel in certain embodiments, (iv) demodulating the down converted and band-limited signal, (v) performing error correction, and (vi) demultiplexing to select the desired stream of data packets. The RF portion of various embodiments includes one or more elements to perform these functions, for example, frequency selectors, signal selectors, bandlimiters, channel selectors, filters, downconverters, demodulators, error correctors, and demultiplexers. The RF portion may include a tuner that performs various of these functions, including, for example, down converting the received signal to a lower frequency (for example, an intermediate frequency or a near-baseband frequency) or to baseband. In one set-top box embodiment, the RF portion and its associated input processing element receives an RF signal transmitted over a wired (for example, cable) medium, and performs frequency selection by filtering, down converting, and filtering again to a desired frequency band. Various embodiments rearrange the order of the above-described (and other) elements, remove some of these elements, and/or add other elements performing similar or different functions. Adding elements may include inserting elements in between existing elements, for example, inserting amplifiers and an analog- to-digital converter. In various embodiments, the RF portion includes an antenna.

[33] Additionally, the USB and/or HDMI terminals may include respective interface processors for connecting system 100 to other electronic devices across USB and/or HDMI connections. It is to be understood that various aspects of input processing, for example, Reed-Solomon error correction, may be implemented, for example, within a separate input processing IC or within processor 110 as necessary. Similarly, aspects of USB or HDMI interface processing may be implemented within separate interface Ics or within processor 110 as necessary. The demodulated, error corrected, and demultiplexed stream is provided to various processing elements, including, for example, processor 110, and encoder/decoder 130 operating in combination with the memory and storage elements to process the datastream as necessary for presentation on an output device.

[34] Various elements of system 100 may be provided within an integrated housing, Within the integrated housing, the various elements may be interconnected and transmit data therebetween using suitable connection arrangement 115, for example, an internal bus as known in the art, including the I2C bus, wiring, and printed circuit boards.

[35] The system 100 includes communication interface 150 that enables communication with other devices via communication channel 190. The communication interface 150 may include, but is not limited to, a transceiver configured to transmit and to receive data over communication channel 190. The communication interface 150 may include, but is not limited to, a modem or network card and the communication channel 190 may be implemented, for example, within a wired and/or a wireless medium.

[36] Data is streamed to the system 100, in various embodiments, using a Wi-Fi network such as IEEE 802. 11. The Wi-Fi signal of these embodiments is received over the communications channel 190 and the communications interface 150 which are adapted for Wi-Fi communications. The communications channel 190 of these embodiments is typically connected to an access point or router that provides access to outside networks including the Internet for allowing streaming applications and other over-the-top communications. Other embodiments provide streamed data to the system 100 using a set-top box that delivers the data over the HDMI connection of the input block 105. Still other embodiments provide streamed data to the system 100 using the RF connection of the input block 105.

[37] The system 100 may provide an output signal to various output devices, including a display 165, speakers 175, and other peripheral devices 185. The other peripheral devices 185 include, in various examples of embodiments, one or more of a stand-alone DVR, a disk player, a stereo system, a lighting system, and other devices that provide a function based on the output of the system 100. In various embodiments, control signals are communicated between the system 100 and the display 165, speakers 175, or other peripheral devices 185 using signaling such as AV. Link, CEC, or other communications protocols that enable device-to-device control with or without user intervention. The output devices may be communicatively coupled to system 100 via dedicated connections through respective interfaces 160, 170, and 180. Alternatively, the output devices may be connected to system 100 using the communications channel 190 via the communications interface 150. The display 165 and speakers 175 may be integrated in a single unit with the other components of system 100 in an electronic device, for example, a television. In various embodiments, the display interface 160 includes a display driver, for example, a timing controller (T Con) chip. [38] The display 165 and speaker 175 may alternatively be separate from one or more of the other components, for example, if the RF portion of input 105 is part of a separate set-top box. In various embodiments in which the display 165 and speakers 175 are external components, the output signal may be provided via dedicated output connections, including, for example, HDMI ports, USB ports, or COMP outputs.

[39] FIG. 2 illustrates an example of a block-based hybrid video encoder 200. Before being encoded, the video sequence may go through pre-encoding processing (201), for example, applying a color transform to the input color picture (e.g., conversion from RGB 4:4:4 to YCbCr 4:2:0), or performing a remapping of the input picture components in order to get a signal distribution more resilient to compression (for instance using a histogram equalization of one of the color components). Metadata can be associated with the pre-processing, and attached to the bitstream.

[40] In the encoder 200, a picture is encoded by the encoder elements as described below. The picture to be encoded is partitioned (202) and processed in units of, for example, CUs (Coding Units). Each unit is encoded using, for example, either an intra or inter mode. When a unit is encoded in an intra mode, it performs intra prediction (260). In an inter mode, motion estimation (275) and compensation (270) are performed. The encoder decides (205) which one of the intra mode or inter mode to use for encoding the unit, and indicates the intra/inter decision by, for example, a prediction mode flag. Prediction residuals are calculated, for example, by subtracting (210) the predicted block from the original image block.

[41] The prediction residuals are then transformed (225) and quantized (230). The quantized transform coefficients, as well as motion vectors and other syntax elements such as the picture partitioning information, are entropy coded (245) to output a bitstream. The encoder can skip the transform and apply quantization directly to the non-transformed residual signal. The encoder can bypass both transform and quantization, i.e., the residual is coded directly without the application of the transform or quantization processes.

[42] The encoder decodes an encoded block to provide a reference for further predictions. The quantized transform coefficients are de-quantized (240) and inverse transformed (250) to decode prediction residuals. Combining (255) the decoded prediction residuals and the predicted block, an image block is reconstructed. In-loop filters (265) are applied to the reconstructed picture to perform, for example, deblocking/SAO (Sample Adaptive Offset)/ ALF (Adaptive Loop Filter) filtering to reduce encoding artifacts. The filtered image is stored in a reference picture buffer (280).

[43] FIG. 3 illustrates a block diagram of an example video decoder 300. In the decoder 300, a bitstream is decoded by the decoder elements as described below. Video decoder 300 generally performs a decoding pass reciprocal to the encoding pass as described in FIG. 2. The encoder 200 also generally performs video decoding as part of encoding video data.

[44] In particular, the input of the decoder includes a video bitstream, which can be generated by video encoder 200. The bitstream is first entropy decoded (330) to obtain transform coefficients, prediction modes, motion vectors, and other coded information. The picture partition information indicates how the picture is partitioned. The decoder may therefore divide (335) the picture according to the decoded picture partitioning information. The transform coefficients are dequantized (340) and inverse transformed (350) to decode the prediction residuals. Combining (355) the decoded prediction residuals and the predicted block, an image block is reconstructed. The predicted block can be obtained (370) from intra prediction (360) or motion-compensated prediction (i.e., inter prediction) (375). In-loop filters (365) are applied to the reconstructed image. The filtered image is stored at a reference picture buffer (380). Note that, for a given picture, the contents of the reference picture buffer 380 on the decoder side is identical to the contents of the reference picture buffer 280 on the encoder side for the same picture.

[45] The decoded picture can further go through post-decoding processing (385), for example, an inverse color transform (e.g., conversion from YCbCr 4:2:0 to RGB 4:4:4) or an inverse remapping performing the inverse of the remapping process performed in the pre-encoding processing (201). The post-decoding processing can use metadata derived in the pre-encoding processing and signaled in the bitstream.

[46] Cross-component linear model (CCLM) for intra prediction

[47] To reduce the cross-component redundancy, a Cross-Component Linear Model (CCLM) prediction mode is used in the VVC, for which the chroma samples are predicted based on the reconstructed luma samples of the same CU by using a linear model as follows: pred_c(i, j) = a • rec_L(i, j) + p (eq.l) where pred_c(i,j) represents the predicted chroma samples in a CU and rec_L(i, j) represents the down-sampled reconstructed luma samples of the same CU. In VVC, the CCLM parameters (a and P) are derived with at most four neighboring chroma samples and their corresponding down- sampled luma samples. There exists three CCLM modes (LM CHROMA, MDLM T, MDLM L) that differ in the location of the neighboring chroma samples, as depicted in FIG. 4. The CCLM mode to be used is coded per CU.

[48] The four neighboring luma samples at the selected positions are down-sampled and compared four times to find two smaller values: X°A and x and two larger values: X°B and X^JB. Their corresponding chroma sample values are denoted as y°A, y^XA, y°B and y^XB. Then X_a, Xb, Y_a and Yb are derived as:

X_a= (X°A + X^XA +1) » 1; Xb = (X°B + X^XB +1) » 1;

Y_a= (y°A + y^XA +1) » 1; Yb = (y°B + y^XB +1) » 1 (eq.2)

[49] Finally, the linear model parameters a and P are obtained according to the following equations. a = (Y_a - Yb)/(X_a- X_b) (eq.3)

P = Yb - a X_b (eq.4)

[50] Multi-model LM (MMLM) and other CCLM variants

[51] There exist several variants to CCLM, where a) the location and/or the number of the neighboring samples used to derive the model, b) the method to derive the linear model parameters (a, P), or c) the luma down-sampling filter, may differ. For example, in ECM (Enhanced Compression Model), the CCLM included in VVC is extended by adding three Multi-model LM (MMLM) modes. In each MMLM mode, the reconstructed neighboring samples are classified into two classes using a threshold which is the average of the luma reconstructed neighboring samples, as shown in FIG. 5. The linear model of each class is derived using the Least-Mean- Square (LMS) method or the previous CCLM method for example.

[52] Convolutional cross-component model (CCCM) for intra prediction

[53] The convolutional cross-component model (CCCM) predicts chroma samples from reconstructed luma samples in a similar spirit as done by CCLM. As with CCLM, the reconstructed luma samples may be down-sampled to match the lower resolution of the chroma grid when chroma sub-sampling is used.

[54] Also, similarly to CCLM, there is an option of using a single model or multi-model variant of CCCM. The multi-model variant uses two models, one model derived for samples above the average luma reference value and another model for the rest of the samples (following the spirit of the CCLM design). In a variant, the multi-model CCCM mode can be selected for PUs which have at least 128 reference samples available.

[55] A pixel can include several (e.g., three) color components. For ease of notations, the luma component may be called the luma sample and the chroma component may be called the chroma sample, and the luma sample and chroma sample representing the same pixel are considered as collocated. Similarly, for an image block with several color components, the luma block and chroma block of this image block are considered as collocated.

[56] CCCM uses a convolutional 7-tap filter consisting of a 5-tap plus sign shape spatial component, a nonlinear term and a bias term. The input to the spatial 5-tap component of the filter consists of a center (C) luma sample which is collocated with the chroma sample to be predicted and its above/north (N), below/south (S), left/west (W) and right/east (E) neighbors as illustrated in FIG. 6.

[57] The nonlinear term P is represented as power of two of the center luma sample C, offset by midVai = (1 « (bitDepth - 1)) and scaled to the sample value range of the content:

P = ( C*C + midVai ) » bitDepth.

That is, for 10-bit content midVai is 512 and P is calculated as:

P = ( C*C + 512 ) » 10.

[58] The bias term B represents a scalar offset between the input and output (similarly to the offset term in CCLM) and is set to the middle chroma value (512 for 10-bit content).

[59] Output of the filter is calculated as a convolution between the filter coefficients Ci and the input values and clipped to the range of valid chroma samples: predChromaVal = coC + ciN + C2S + C3E + C4W + csP + ceB.

[60] The filter coefficients Ci are calculated by minimizing MSE (Mean Squared Error) between predicted and reconstructed chroma samples in the reference area. FIG. 7 illustrates the reference area which consists of 6 lines/columns of chroma samples above and left of the PU. Reference area extends one PU width to the right and one PU height below the PU boundaries. The reference area is adjusted to include only available samples. One additional line and column of samples are attached to the reference area (705) to support the “side samples” of the plus shaped spatial filter and are padded when in unavailable areas.

[61] Denote

The MSE minimization is performed by calculating an autocorrelation matrix =Z ' xearea ^ai ^x) - ctj (x) for the luma input and a cross-correlation vector between the luma input and chroma output.

[62] The autocorrelation matrix is LDL decomposed and the final filter coefficients are calculated using back- substitution. The process follows roughly the calculation of the ALF filter coefficients in ECM, however LDL decomposition (i.e., alternative Cholesky decomposition) was chosen instead of Cholesky decomposition to avoid using square root operations. The calculation uses only integer arithmetic.

[63] An overview of the process of predicting chroma samples using a cross-component luma model (e.g., CCLM, MMLM, CCCM) is illustrated in FIG. 8. In particular, at step 810, the reference samples (reconstructed luma and chroma sample values) are selected from the neighborhood of the current CU. The reconstructed luma sample values may be filtered to obtain down-sampled luma samples. At step 820, in case of multi-model, a threshold is determined to classify the reference samples into at least two classes. At step 830, the parameters of the models are derived from the reference luma and chroma sample values. At step 840, the CC-model(s) allow deriving the chroma sample prediction values from the collocated (down-sampled) reconstructed luma sample values of the current block, for example, using CCLM, MMLM or CCCM. In one example, when the luma is down-sampled by a factor of 2, the prediction of chroma sample C(x,y) uses the reconstructed luma sample at same position L(x,y), and samples at positions L(x+l,y), L(x-l,y), L(x,y+1), L(x,y-1), as shown in FIG. 6. [64] In a variant named GL-CCCM, the GL-CCCM filter for the prediction is: predChromaVal = coC + ciGy + C2Gx + csY + C4X + csP + ceB, where Y and X parameters are the vertical and horizontal locations of the center luma sample and they are calculated with respect to the top-left coordinates of the block, and Gy and Gx are the vertical and horizontal gradients, respectively, and are calculated as:

Gy = (2N + NW + NE) - (2S + SW + SE),

Gx = (2W + NW + SW) - (2E + NE + SE), where FIG. 9 illustrates the sample positions for samples NW, NE, SW and SE.

[65] Selection of the location of the reference samples used to derive the CC-model

[66] In another variant, the neighboring region used to select the reference samples for deriving the CC-model (Cross-Component-model) may be selected among a set of pre-defined regions. For example, in ECM-6.0-EE2 and ECM-7.0, one can signal in the bitstream the selected region among {above-left, above, left}, corresponding to the modes {CHROMA IDX, T IDX, L IDX} respectively, together with the information indicating a single model or multi-model mode {MDLM, MMLM} as depicted in FIG. 10.

[67] Decoder-side Intra Mode Derivation (PIMP)

[68] DIMD derives the indices of two intra prediction modes that are likely the two best intra prediction modes for predicting the current luminance CB in terms of rate-distortion, from the gradients in a template of decoded reference samples of the current luminance CB to be encoded/decoded. Later, the current luminance CB is predicted by blending the two predicted blocks obtained by applying the two derived intra prediction modes with the predicted block obtained by applying PLANAR. The weights involved in the blending are derived from the gradients in this template.

[69] Template-based Intra Mode Perivation

[70] Like DIMD, for the current luminance CB to be encoded/decoded, TIMD follows a two- step process: an intra prediction mode index derivation step involving a template of decoded reference samples of the current luminance CB and a step in which the current luminance CB is actually predicted.

[71] In particular, for a given luminance CB, the mode derivation via TIMD applies the same way on the encoder and decoder sides. For each intra prediction mode in the MPM list of this luminance CB, if needed, supplemented with default modes, this mode computes a prediction of the template of the luminance CB from the decoded reference samples of the template, and the SATD (Sum of Absolute Transformed Differences) between this prediction and the template of this luminance CB is calculated. The two intra prediction modes with the minimum SATDs are selected as the TIMD modes.

[72] As described above, the cross-components intra prediction tools (e.g., CCLM, MMLM or CCCM) derive the cross-component model from a set of reference reconstructed luma and chroma samples situated in the neighborhood of the current CU. In general, the cross-component model describes a relation between the luma samples and reconstructed chroma samples. Using the crosscomponent model parameters derived based on the luma and chroma samples of a neighboring region, the chroma samples of the current block can be predicted from the luma samples of the current block. Because the cross-component model parameters are derived at both the encoder and decoder, these model parameters need not to be signaled explicitly and therefore can reduce signaling overhead. However, the signal characteristics (statistics) of these luma reference samples may differ from the signal characteristics of the reconstructed luma samples in the current CU, which may impact the accuracy of the derived model.

[73] In this document, it is proposed to modify the process of selecting the reference samples used to derive the CC model to better match the signal characteristics of the reconstructed luma samples of the current CU, for example, using at least one of histogram of the neighboring samples, shape of the current CU and the intra mode direction of the current CU.

[74] As described above, the signal properties of some reconstructed luma sample values in the neighborhood of the current CU may differ significantly from the signal properties of the reconstructed luma sample values of the current CU. This may impact the efficiency of the derived CC model to predict the chroma samples values from the reconstructed CU luma sample values. For example, in FIG. 11 A and FIG. 1 IB, (luma, chroma) sample values of the current CU can only be (LI, Cl) and (L3, C3), and the (luma, chroma) sample values of the reference samples can be (L0, CO), (LI, Cl) and (L3, C3). That is, the signal characteristics of the reference samples is different from the current CU, since the luma value L0 is not in the current CU. Including (L0, CO) or not in the reference samples to derive the CC model may lead to different linear models as shown in FIG. 11 A and FIG. 1 IB.

[75] The proposed methods may apply to cross-components intra prediction tools (CC mode), for example, to CCLM, MMLM or CCCM. In general, these methods use at least one of histogram matching of the neighboring reconstructed luma samples, the shape of the current CU, and the intra mode direction to derive the CC-model.

[76] Select the region to derive CC-model with histogram matching

[77] In this embodiment, the selection of the neighboring region, which contains the reference samples used to derive the CC-model, is made using histogram matching between the reconstructed luma samples of the current CU and the reconstructed luma samples of the neighboring region candidates.

[78] FIG. 13 illustrates a process (1300) of selecting the region used to derive the CC-model using histogram matching, according to an embodiment. In this embodiment, first the histogram “curHist” of the reconstructed luma samples of the current block is collected (1305). Next, for each (1360) available neighboring pre-defined regions (e.g., as depicted in FIG.10 or FIG.15), the histogram “iHist” of region “i” is computed (1320) and the histogram matching score with curHist is computed (1330). The histogram matching measures the dissimilarity of the two histograms. For example, it can be the sum of differences at different bins of the normalized cumulated histograms as depicted in FIG. 12. The region with the best matching histogram with curHist (1340) is selected (1350) for deriving the CC-model.

[79] This process can be performed at the encoder or decoder. As other cross-component intra prediction tools, because the CC-model parameters and the selected region can be derived at the decoder, these parameters need not to be transmitted in the bitstream.

[80] The condition (1310) checks whether the region is available. For example, the region is unavailable if all the samples or a subset of the samples are outside of the current picture, or if they belong to another slice, tile, sub-picture, etc. In a variant, if the region is partially unavailable, only the available samples are used to derive the CC-model. In another variant, only samples coded in intra are used to avoid pipeline dependency with inter coded CUs.

[81] In a variant, the histogram matching score is computed on the same range as the current reconstructed luma samples. That is, the differences are only calculated for the bins falling into the data range of the reconstructed luma samples of the current CU. For example, the range is a range of the current CU reconstructed luma sample values [min, max] where min is the lowest (minimum) luma value in the current CU and max is the highest (maximum) luma value in the current CU.

[82] Adjustment of the reference region

[83] FIG. 14 illustrates a process (1400) of deriving region variants from a primary list of three regions, according to an embodiment. In this embodiment, the neighboring region is signaled (1405) from a primary list of regions. The signaled neighboring region shape is adjusted/refined using histogram matching. A set of region variants may be pre-defined for each primary region. For example, FIG. 15 depicts three primary regions {Above, Left, Above-Left} and their associated variants. In one example, a region variant can be obtained by adding/removing a relatively small portion of samples versus the primary number of samples, the portion being situated spatially close to the primary region and/or in the same direction (e.g., Above, Left or Above-Left). The size of a region variant can be scaled up or down based on the size of the primary region.

[84] In particular, the condition (1410) checks whether a region variant is available. For each (1460) available region variant, the histogram “xHist” of region variant “x” is computed (1420) and the histogram matching score with curHist is computed (1430). The region variant with the best matching histogram with curHist (1440) is selected (1450) as the refined region to derive the CC-model.

[85] Refining a region with region variants can be used with other processes. For example, in process 1300, after the best region is selected, it can also further choose a region variant.

[86] Signaling the histogram-based mode

[87] In this embodiment, a flag is signaled to indicate which method applies. If the flag is 0, then the regular method is used, meaning that the default (primary) region is used to derive the CC-model.

[88] Deriving multi-model threshold with median

[89] As described before, in case of multi-model, the threshold is derived using the average of the reference neighboring samples. In this embodiment, the threshold is computed as the median of the selected neighboring reference samples. The median can be derived straightforwardly using the cumulated histogram. In addition, a flag may be signaled per CU or per slice for example, to indicate if this method is used.

[90] Reducing multi-model gap

[91] To reduce the CC-model discontinuity, one can derive a separate threshold for the samples below the average (model-1) and the samples above (model-2). First, a preliminary threshold is derived using the average of the selected neighboring samples. Next, an additional set of neighboring samples values above (model-1) the threshold is used to derive the CC-model of the model- 1, and an additional set of neighboring samples values below (model-2) the threshold is used to derive the CC-model of model-2. The value of ratio (relative number of additional samples) may be 10% for example.

[92] In the example depicted in FIG. 16, model- 1 is computed with the reference samples below the threshold plus additional samples (hatched) above the threshold, whereas model-2 is computed with the reference samples above the threshold plus additional samples (hatched) below the threshold. For each model, the number of additional samples may be determined as being equal to N% of the number of samples below (or above respectively) the threshold for example. Then the hatched samples have been used to compute both models, whereas the black and white samples have been used for models 1 and 2, respectively. The dashed and the full lines represent the derived models without using the additional samples and using the additional samples, respectively. One can observe that the gap at the threshold position between the two models is reduced with the proposed method. In addition, a flag may be signaled per CU or per slice to indicate if this method is used.

[93] Using intra mode direction

[94] The intra mode of the collocated luma block can be used to decide the region used for CC. In case that the mode is horizontally oriented (or near to horizontal mode), left region can be used. Otherwise, if the mode is vertically oriented (or near vertical mode), above region can be used. Similarly, if the mode is diagonally oriented both regions will be used. The reasoning behind this method is that if the used mode is horizontally oriented, there is some correlation between the current luma block and its left region. Therefore, it is expected that the CC model will have the same correlation. The inverse is also true for vertically or diagonally oriented intra mode.

[95] In a variant, DIMD can be used. That is, the DIMD derived mode of the luma collocated block can be used in the same manner as before to decide the region. This is especially useful when the intra mode direction is not available (collocated luma is inter prediction, IBC, MIP, etc).

[96] In another variant, chroma DIMD can be used. Chroma DIMD is based on analyzing the templates of the luma and the two chroma channels. The resulting DIMD mode can be used similarly. Instead of DIMD, TIMD (template-based intra mode derivation) can also be used.

[97] Considering the block dimensions

[98] The block dimension can also give a clue on the used region. For a tall block (whose height is larger than its width), it is less probably that the above region can be used. This is because the bottom samples will be far from in the above reference region. Therefore, it is expected to either use the full region or the left region, or left region only. Similarly, for the long blocks (width larger than height), it is expected to either use the above or full region, or above only. Finally, for square blocks, all regions (above, left or full) can be used. Whether a block is considered as tall/long may be determined based on a threshold of the ratio of the width and height.

[99] In ECM, the region used to signal the reference region is coded together with single/multi- model as follows:

Table 1. Signaling reference region in ECM-6.0

[100] This method may be used to select the region a-priori and save signaling bits. In a variant, this method may be used to derive the most probable region that will be ordered at first.

[101] In a variant, it is proposed to use this method to reduce the signaling of the used region. Specifically, a reduced signaling is used for non-square blocks to indicate which region is used. If the signaling is not left and above region, the region can be deduced. For example, for a tall vertically oriented block, if the signaling is not left and above region, the left region is used as it is the most probable one (Table 2).

Table 2. Example of modified signaling of the reference region for horizontal/vertical shapes

[102] In a variant, a separate flag for signaling single/multi-model may be used.

[103] In another variant, the shape of the reference region may be adapted depending on the current CU shape, as depicted in examples 1 and 2 in FIG. 17 for likely square and likely horizontal long shapes (vertical thin shapes may be deduced by symmetry).

[104] Various methods are described herein, and each of the methods comprises one or more steps or actions for achieving the described method. Unless a specific order of steps or actions is required for proper operation of the method, the order and/or use of specific steps and/or actions may be modified or combined. Additionally, terms such as “first”, “second”, etc. may be used in various embodiments to modify an element, component, step, operation, etc., for example, a “first decoding” and a “second decoding”. Use of such terms does not imply an ordering to the modified operations unless specifically required. So, in this example, the first decoding need not be performed before the second decoding, and may occur, for example, before, during, or in an overlapping time period with the second decoding.

[105] Various methods and other aspects described in this application can be used to modify modules, for example, the intra prediction modules (260, 360), of a video encoder 200 and decoder 300 as shown in FIG. 2 and FIG. 3. Moreover, the present aspects are not limited to ECM, VVC or HEVC, and can be applied, for example, to other standards and recommendations, and extensions of any such standards and recommendations. Unless indicated otherwise, or technically precluded, the aspects described in this application can be used individually or in combination. [106] Various numeric values are used in the present application. The specific values are for example purposes and the aspects described are not limited to these specific values.

[107] Various implementations involve decoding. “Decoding,” as used in this application, may encompass all or part of the processes performed, for example, on a received encoded sequence in order to produce a final output suitable for display. In various embodiments, such processes include one or more of the processes typically performed by a decoder, for example, entropy decoding, inverse quantization, inverse transformation, and differential decoding. Whether the phrase “decoding process” is intended to refer specifically to a subset of operations or generally to the broader decoding process will be clear based on the context of the specific descriptions and is believed to be well understood by those skilled in the art.

[108] Various implementations involve encoding. In an analogous way to the above discussion about “decoding”, “encoding” as used in this application may encompass all or part of the processes performed, for example, on an input video sequence in order to produce an encoded bitstream.

[109] The implementations and aspects described herein may be implemented in, for example, a method or a process, an apparatus, a software program, a data stream, or a signal. Even if only discussed in the context of a single form of implementation (for example, discussed only as a method), the implementation of features discussed may also be implemented in other forms (for example, an apparatus or program). An apparatus may be implemented in, for example, appropriate hardware, software, and firmware. The methods may be implemented in, for example, an apparatus, for example, a processor, which refers to processing devices in general, including, for example, a computer, a microprocessor, an integrated circuit, or a programmable logic device. Processors also include communication devices, for example, computers, cell phones, portable/personal digital assistants (“PDAs”), and other devices that facilitate communication of information between end-users.

[HO] Reference to “one embodiment” or “an embodiment” or “one implementation” or “an implementation”, as well as other variations thereof, means that a particular feature, structure, characteristic, and so forth described in connection with the embodiment is included in at least one embodiment. Thus, the appearances of the phrase “in one embodiment” or “in an embodiment” or “in one implementation” or “in an implementation”, as well any other variations, appearing in various places throughout this application are not necessarily all referring to the same embodiment.

[111] Additionally, this application may refer to “determining” various pieces of information. Determining the information may include one or more of, for example, estimating the information, calculating the information, predicting the information, or retrieving the information from memory.

[112] Further, this application may refer to “accessing” various pieces of information. Accessing the information may include one or more of, for example, receiving the information, retrieving the information (for example, from memory), storing the information, moving the information, copying the information, calculating the information, determining the information, predicting the information, or estimating the information.

[113] Additionally, this application may refer to “receiving” various pieces of information. Receiving is, as with “accessing”, intended to be a broad term. Receiving the information may include one or more of, for example, accessing the information, or retrieving the information (for example, from memory). Further, “receiving” is typically involved, in one way or another, during operations, for example, storing the information, processing the information, transmitting the information, moving the information, copying the information, erasing the information, calculating the information, determining the information, predicting the information, or estimating the information.

[114] It is to be appreciated that the use of any of the following “/”, “and/or”, and “at least one of’, for example, in the cases of “A/B”, “A and/or B” and “at least one of A and B”, is intended to encompass the selection of the first listed option (A) only, or the selection of the second listed option (B) only, or the selection of both options (A and B). As a further example, in the cases of “A, B, and/or C” and “at least one of A, B, and C”, such phrasing is intended to encompass the selection of the first listed option (A) only, or the selection of the second listed option (B) only, or the selection of the third listed option (C) only, or the selection of the first and the second listed options (A and B) only, or the selection of the first and third listed options (A and C) only, or the selection of the second and third listed options (B and C) only, or the selection of all three options (A and B and C). This may be extended, as is clear to one of ordinary skill in this and related arts, for as many items as are listed.

[115] Also, as used herein, the word “signal” refers to, among other things, indicating something to a corresponding decoder. For example, in certain embodiments the encoder signals which neighbor region is selected. In this way, in an embodiment the same parameter is used at both the encoder side and the decoder side. Thus, for example, an encoder can transmit (explicit signaling) a particular parameter to the decoder so that the decoder can use the same particular parameter. Conversely, if the decoder already has the particular parameter as well as others, then signaling can be used without transmitting (implicit signaling) to simply allow the decoder to know and select the particular parameter. By avoiding transmission of any actual functions, a bit savings is realized in various embodiments. It is to be appreciated that signaling can be accomplished in a variety of ways. For example, one or more syntax elements, flags, and so forth are used to signal information to a corresponding decoder in various embodiments. While the preceding relates to the verb form of the word “signal”, the word “signal” can also be used herein as a noun.

[116] As will be evident to one of ordinary skill in the art, implementations may produce a variety of signals formatted to carry information that may be, for example, stored or transmitted. The information may include, for example, instructions for performing a method, or data produced by one of the described implementations. For example, a signal may be formatted to carry the bitstream of a described embodiment. Such a signal may be formatted, for example, as an electromagnetic wave (for example, using a radio frequency portion of spectrum) or as a baseband signal. The formatting may include, for example, encoding a data stream and modulating a carrier with the encoded data stream. The information that the signal carries may be, for example, analog or digital information. The signal may be transmitted over a variety of different wired or wireless links, as is known. The signal may be stored on a processor-readable medium.

Claims

1. A method of video encoding, comprising: obtaining reconstructed luma samples of a block of a picture; selecting, from a plurality of candidate neighboring regions for said block, one neighboring region for said block; obtaining reconstructed luma samples and reconstructed chroma samples of said selected neighboring region; obtaining model parameters of a cross-component model that models a relation between said reconstructed luma samples and reconstructed chroma samples of said selected neighboring region; predicting chroma samples of said block based on said reconstructed luma samples of said block and said cross-component model with said model parameters; and encoding said chroma samples of said block based on said predicted chroma samples.

2. A method of video decoding, comprising: obtaining reconstructed luma samples of a block of a picture; selecting, from a plurality of candidate neighboring regions for said block, one neighboring region for said block; obtaining reconstructed luma samples and reconstructed chroma samples of said selected neighboring region; obtaining model parameters of a cross-component model that models a relation between said reconstructed luma samples and reconstructed chroma samples of said selected neighboring region; predicting chroma samples of said block based on said reconstructed luma samples of said block and said cross-component model with said model parameters; and decoding said chroma samples of said block based on said predicted chroma samples.

3. The method of claim 1 or 2, further comprising: performing histogram matching between said reconstructed luma samples of said block and reconstructed luma samples of each of said plurality of neighboring regions of said block, wherein a neighboring region with best histogram matching is selected.

4. The method of any one of claims 1-3, further comprising: obtaining a range of values of said reconstructed samples of said block, wherein histogram matching is only performed within said range of values.

5. The method of any one of claims 1, 3 and 4, further comprising encoding a signal to indicate that said neighboring region is selected.

6. The method of any one of claims 2, 3 and 4, further comprising decoding a signal indicative of said selected neighboring region.

7. The method of any one of claims 1-6, further comprising: refining said selected neighboring region by removing one or more samples from said selected neighboring region.

8. The method of any one of claims 1-7, wherein said cross-component model is a multi-model, and wherein said obtaining model parameters comprises: obtaining a threshold based on a median of said reconstructed luma samples of said selected neighboring region for said multi -model.

9. The method of claim 8, wherein two models are used in said multi-model, and wherein one or more samples above or below said threshold are used for obtaining model parameters for said two models.

10. The method of any one of claims 1-9, wherein only a left neighboring region is selected responsive to that an intra prediction mode of said block is horizontally oriented.

11. The method of any one of claims 1-9, wherein only an above neighboring region is selected responsive to that an intra prediction mode of said block is horizontally oriented.

12. The method of claim 10 or 11, wherein said intra prediction mode of said block is obtained by DIMD or TIMD.

13. The method of any one of claims 1-9, wherein only a left region is selected responsive to that said block is a tall block.

14. The method of any one of claims 1-9, wherein only an above region is selected responsive to that said block is a long block.

15. An apparatus for video encoding, comprising at least one memory and one or more processors, wherein said one or more processors are configured to: obtain reconstructed luma samples of a block of a picture; select, from a plurality of candidate neighboring regions for said block, one neighboring region for said block; obtain reconstructed luma samples and reconstructed chroma samples of said selected neighboring region; obtain model parameters of a cross-component model that models a relation between said reconstructed luma samples and reconstructed chroma samples of said selected neighboring region; predict chroma samples of said block based on said reconstructed luma samples of said block and said cross-component model with said model parameters; and encode said chroma samples of said block based on said predicted chroma samples.

16. An apparatus for video decoding, comprising at least one memory and one or more processors, wherein said one or more processors are configured to: obtain reconstructed luma samples of a block of a picture; select, from a plurality of candidate neighboring regions for said block, one neighboring region for said block; obtain reconstructed luma samples and reconstructed chroma samples of said selected neighboring region; obtain model parameters of a cross-component model that models a relation between said reconstructed luma samples and reconstructed chroma samples of said selected neighboring region; predict chroma samples of said block based on said reconstructed luma samples of said block and said cross-component model with said model parameters; and decode said chroma samples of said block based on said predicted chroma samples.

17. The apparatus of claim 15 or 16, wherein said one or more processors are further configured to: perform histogram matching between said reconstructed luma samples of said block and reconstructed luma samples of each of said plurality of neighboring regions of said block, wherein a neighboring region with best histogram matching is selected.

18. The apparatus of any one of claims 15-17, wherein said one or more processors are further configured to: obtain a range of values of said reconstructed samples of said block, wherein histogram matching is only performed within said range of values.

19. The apparatus of any one of claims 15, 17 and 18, wherein said one or more processors are further configured to encode a signal to indicate that said neighboring region is selected.

20. The apparatus of any one of claims 16, 17 and 18, wherein said one or more processors are further configured to decode a signal indicative of said selected neighboring region.

21. The apparatus of any one of claims 15-20, wherein said one or more processors are further configured to: refine said selected neighboring region by removing one or more samples from said selected neighboring region.

22. The apparatus of any one of claims 15-22, wherein said cross-component model is a multi-model, and wherein said one or more processors are configured to obtain model parameters by performing: obtaining a threshold based on a median of said reconstructed luma samples of said selected neighboring region for said multi -model.

23. The apparatus of claim 22, wherein two models are used in said multi-model, and wherein one or more samples above or below said threshold are used for obtaining model parameters for said two models.

24. The apparatus of any one of claims 15-24, wherein only a left neighboring region is selected responsive to that an intra prediction mode of said block is horizontally oriented.

25. The apparatus of any one of claims 15-23, wherein only an above neighboring region is selected responsive to that an intra prediction mode of said block is horizontally oriented.

26. The apparatus of claim 24 or 25, wherein said intra prediction mode of said block is obtained by DIMD or TIMD.

27. The apparatus of any one of claims 15-23, wherein only a left region is selected responsive to that said block is a tall block.

28. The apparatus of any one of claims 15-23, wherein only an above region is selected responsive to that said block is a long block.

29. A signal comprising video data, formed by performing the method of any one of claims 1 and 3-14.

30. A computer readable storage medium having stored thereon instructions for encoding or decoding a video according to the method of any one of claims 1-14.