DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 07/28/2026 has been entered.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 2-7, 9-12, 15-19 and 22 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zhang et al. (US 20180077426 A1).
Regarding claim 2, Zhang teaches the claim as follows:
A method for video decoding, the method comprising: receiving coded information of a current chroma block and a luma block that is collocated with the current chroma block (¶0101: During the decoding process, video decoder 30 receives an encoded video bitstream that represents video blocks of an encoded video slice and associated syntax elements from video encoder 20. ¶0058: a video picture may be divided into a sequence of coding tree units (CTUs) (or largest coding units (LCUs)) that may include both luma and chroma samples…A video picture may be partitioned into one or more slices. Each CTU may be split into coding units (CUs) according to a quadtree. ¶0065: For intra coding, partitions of a CU, or the CU itself, may be collocated with a corresponding leaf-TU for the CU); determining a feature value based on at least one of (i) neighboring reconstructed chroma samples of the current chroma block and (ii) neighboring reconstructed luma samples of the luma block that is collocated with the current chroma block (Figs. 11-13, ¶0139: video encoder 20 and video decoder 30 may be configured to calculate the Threshold as the average value of the neighboring coded (also be denoted as ‘reconstructed’) luma samples); grouping chroma samples of the current chroma block and luma samples of the luma block that are collocated with the current chroma block into a plurality of groups based on a threshold of the feature value, each of the plurality of groups including a respective chroma sample and a respective luma sample (Figs. 7A-7E, 11-13, ¶0129: Neighboring luma samples and neighboring chroma samples of the current block may be classified into several groups based on the values of the samples. ¶0135: T1−TM-1 are threshold levels for each classification group); determining a respective cross-component prediction mode from a plurality of candidate modes for each of the plurality of groups by comparing the respective chroma sample and the respective luma sample of each respective group to the determined feature value (¶0044, 0140: In one example, as illustrated in FIG. 7B, when M is equal to 3, neighboring samples may be classified into three groups. A neighboring sample (e.g., luma sample) with Rec′L[x,y]≦Threshold1 may classified into group 1; a neighboring sample with Threshold1<Rec′.sub.L[x,y]≦Threshold2 may be classified into group 2 and a neighboring sample with Rec′.sub.L[x,y]>Threshold2 may be classified into group 3. See eq (7), ¶0151-0155), each of the plurality of candidate modes corresponding to a distinct mathematical model (¶0256: video decoder 30 may be configured to determine parameters for each of the two or more linear prediction models using luma samples and chroma samples from blocks of video data that neighbor the first block of video data. In one example, video decoder 30 may be configured to classify the reconstructed luma samples that are greater than a first threshold as being in a first sample group of a plurality of sample groups, classify the reconstructed luma samples that are less than or equal to the first threshold as being in a second sample group of the plurality of sample groups…the second linear prediction model being different than the first linear prediction model); determining a respective set of model parameters for each of the plurality of groups, each of the sets of model parameters corresponding to a set of coefficients of the mathematical model of the respective cross-component prediction mode (See for example eq (7) group 1 equation having coefficient α1, group 2 equation having coefficient α2, group 3 equation having coefficient α3); and reconstructing the current chroma block based on the determined cross-component prediction modes and the determined sets of model parameters of the plurality of groups (¶0044, 0258-0259: video decoder 30 may be configured to receive an encoded block of luma samples for a first block of video data (142), decode the encoded block of luma samples to create reconstructed luma samples (144), and predict chroma samples for the first block of video data using the reconstructed luma samples for the first block of video data and two or more linear prediction models…video decoder 30 may be configured to determine parameters for each of the two or more linear prediction models using luma samples and chroma samples from blocks of video data that neighbor the first block of video data. In one example, video decoder 30 may be configured to classify the reconstructed luma samples that are greater than a first threshold as being in a first sample group of a plurality of sample groups, classify the reconstructed luma samples that are less than or equal to the first threshold as being in a second sample group of the plurality of sample groups…).
Regarding claim 3, Zhang teaches the method of claim 2, wherein the determining the respective cross- component prediction mode comprises: determining that the respective cross-component prediction mode for each of the plurality of groups corresponds to a same cross-component prediction mode (¶0138: as illustrated in FIG. 7A, when M is equal to 2, neighboring samples may be classified into two groups. A neighboring sample with Rec′.sub.L[x,y] Threshold may be classified into group 1; while a neighboring sample with Rec′.sub.L[x,y]>Threshold may be classified into group 2).
Regarding claim 4, Zhang teaches the method of claim 3, wherein first parameters of the cross-component prediction mode for a first group of the plurality of groups are different from second parameters of the cross-component prediction mode for a second group of the plurality of groups (¶0140, See for example eq (7): group 1 model parameters α1, β1 ; group 2 model parameters α2, β2; group 3 model parameters α3, β3).
Regarding claim 5, Zhang teaches the method of claim 4, wherein the cross-component prediction mode is a cross-component linear model (CCLM), and the first parameters of the CCLM for the first group of the plurality of groups are different from the second parameters of the CCLM for the second group of the plurality of groups (¶0140, See for example eq (7): group 1model parameters α1, β1; group 2 model parameters α2, β2; group 3 model parameters α3, β3).
Regarding claim 6, Zhang teaches the method of claim 2, wherein the determining the feature value further comprises: determining the feature value as one of an average value of the neighboring reconstructed chroma samples of the current chroma block and an average value of the neighboring reconstructed luma samples of the collocated luma block (Figs. 11-13, ¶0139: video decoder 30 may be configured to calculate the Threshold as the average value of the neighboring coded (also be denoted as ‘reconstructed’) luma samples).
Regarding claim 7, Zhang teaches the method of claim 2, wherein the determining the feature value further comprises: determining the feature value as one of an average gradient value of the neighboring reconstructed chroma samples of the current chroma block and an average gradient value of the neighboring reconstructed luma samples of the collocated luma block (¶0139: video encoder 20 and video decoder 30 may be configured to calculate the Threshold as the average of minV and maxV, wherein minV and maxV are the minimum value and the maximum values, respectively, of the neighboring coded Luma samples).
Regarding claim 9, Zhang teaches the method of claim 2, wherein the grouping further comprises: determining a characteristic value associated with each of the luma samples of the collocated luma block; determining whether the characteristic value associated with the respective luma sample of the luma samples of the collocated luma block is larger than the threshold of the feature value (¶0138: a neighboring sample with Rec′.sub.L[x,y]>Threshold may be classified into group 2) ; grouping (i) the respective luma sample of the luma samples of the collocated luma block and (ii) a chroma sample of the chroma samples of the current chroma block corresponding to the respective luma sample into a first group based on the characteristic value associated with the respective luma sample being larger than the threshold of the feature value (Figs. 9-13, ¶0138: a neighboring sample with Rec′.sub.L[x,y]>Threshold may be classified into group 2) ; and grouping (i) the respective luma sample of the luma samples of the collocated luma block and (ii) the chroma sample of the chroma samples of the current chroma block corresponding to the respective luma sample into a second group based on the characteristic value associated with the respective luma sample being smaller than the threshold of the feature value (¶0138: A neighboring sample with Rec′.sub.L[x,y] Threshold may be classified into group 1).
Regarding claim 10, Zhang teaches the method of claim 9, wherein the characteristic value of the respective luma sample includes one of (i) a luma sample value of the respective luma sample, and (ii) an average value of the neighboring reconstructed luma samples of the collocated luma block (¶0139: In one example in accordance with FIG. 7A (i.e., where two groups are classified), video encoder 20 and video decoder 30 may be configured to calculate the Threshold as the average value of the neighboring coded (also be denoted as ‘reconstructed’) luma samples. ¶0140: as illustrated in FIG. 7B, when M is equal to 3, neighboring samples may be classified into three groups. A neighboring sample (e.g., luma sample) with Rec′.sub.L[x,y]≦Threshold1 may classified into group 1).
Regarding claim 11, Zhang teaches the method of claim 2, wherein the respective cross-component prediction mode includes one of cross-component linear model (CCLM), chroma from luma (CfL), convolutional cross-component model (CCCM), multiple filter linear model (MFLM), gradient linear model (GLM), a combination of CCLM, CfL, CCCM, and an angular intra prediction mode (¶0044: cross-component prediction in video codecs, and more particularly to techniques for linear model (LM) chroma intra prediction. ¶0127: a Multi-model LM (MMLM) method).
Regarding claim 12, Zhang teaches the method of claim 2, wherein the determining the respective cross- component prediction mode for each of the plurality of groups further comprises: determining the respective cross-component prediction mode based on a corresponding flag that is included in the coded information (¶0135: In the example above, T.sub.1−T.sub.M-1 are threshold levels for each classification group, and thus, the threshold levels for each corresponding linear model Pred.sub.c[x,y]=α.sub.M.Math.Rec′.sub.L[x,y]+β.sub.M. ¶0133: video encoder 20 may be configured to signal syntax elements to video decoder 30 which indicate the classification method to be used).
Regarding claim 15, Zhang teaches method of claim 2, wherein the reconstructing the current chroma block further comprises: generating prediction samples for the chroma samples in each of the plurality of groups based on the respective cross-component prediction mode (¶0163: Video encoder 20 and video decoder 30 may be further configured to apply a first linear model (e.g., Model 1 of FIG. 10) to luma samples in the first classification group (represented by black circles in FIG. 13). Video encoder 20 and video decoder 30 may be further configured to apply a second linear model (e.g., Model 2 of FIG. 10) to luma samples in a second classification group (represented by white circles in FIG. 13). ¶0165: As illustrated in FIG. 13, video encoder 20 and video decoder 30 may be configured to apply Model 1 to coded luma samples…to derive the corresponding predicted chroma samples in the current block. Likewise, video encoder 20 and video decoder 30 may be configured to apply Model 2 to coded luma samples…to derive the corresponding predicted chroma samples in the current block); and determining the prediction samples of the current chroma block as a weighted combination of the prediction samples for the chroma samples in each of the plurality of groups (¶0162: Video encoder 20 and video decoder 30 are further configured to compute a weighted average of the two versions of the predicted chroma samples. The weighted average of two prediction blocks (using Model 1 or Model 2) may be treated as the final prediction block of the current chroma block).
Regarding claim 16, Zhang teaches the claim limitation as follows:
A method of video encoding, the method comprising: determining a feature value based on at least one of (i) neighboring reconstructed chroma samples of a current chroma block and (ii) neighboring reconstructed luma samples of a luma block that is collocated with the current chroma block (Figs. 11-13, ¶0044, 0139: video encoder 20 and video decoder 30 may be configured to calculate the Threshold as the average value of the neighboring coded (also be denoted as ‘reconstructed’) luma samples); grouping chroma samples of the current chroma block and luma samples of the luma block that is collocated with the current chroma block into a plurality of groups based on a threshold of the feature value, each of the plurality of groups including a respective chroma sample and a respective luma sample (Figs. 7A-7E, 11-13, ¶0129: Neighboring luma samples and neighboring chroma samples of the current block may be classified into several groups based on the values of the samples. ¶0135: T1−TM-1 are threshold levels for each classification group); determining a respective cross-component prediction mode from a plurality of candidate modes for each of the plurality of groups by comparing the respective chroma sample and the respective luma sample of each respective group to the determined feature value (¶0044, 0140: In one example, as illustrated in FIG. 7B, when M is equal to 3, neighboring samples may be classified into three groups. A neighboring sample (e.g., luma sample) with Rec′L[x,y]≦Threshold1 may classified into group 1; a neighboring sample with Threshold1<Rec′.sub.L[x,y]≦Threshold2 may be classified into group 2 and a neighboring sample with Rec′.sub.L[x,y]>Threshold2 may be classified into group 3. See eq (7), ¶0151-0155), each of the plurality of candidate modes corresponding to a distinct mathematical model (¶0253: video encoder 20 may be configured to classify the reconstructed luma samples that are greater than a first threshold as being in a first sample group of a plurality of sample groups, classify the reconstructed luma samples that are less than or equal to the first threshold as being in a second sample group of the plurality of sample groups…the second linear prediction model being different than the first linear prediction model); determining a respective set of model parameters for each of the plurality of groups, each of the sets of model parameters corresponding to a set of coefficients of the mathematical model of the respective cross-component prediction mode (See for example eq (7) group 1 equation having coefficient α1, group 2 equation having coefficient α2, group 3 equation having coefficient α3); and encoding the current chroma block based on the determined cross-component prediction modes and the sets of model parameters of the plurality of groups ( ¶0253: video encoder 20 may be configured to determine parameters for each of the two or more linear prediction models using luma samples and chroma samples from blocks of video data that neighbor the first block of video data. In one example, video encoder 20 may be configured to classify the reconstructed luma samples that are greater than a first threshold as being in a first sample group of a plurality of sample groups, classify the reconstructed luma samples that are less than or equal to the first threshold as being in a second sample group of the plurality of sample groups).
Regarding claim 17, the claim recites the limitation analogous to claim 3, and is rejected due to the same reason set forth above with respect to claim 3.
Regarding claim 18, the claim recites the limitation analogous to claim 4, and is rejected due to the same reason set forth above with respect to claim 4.
Regarding claim 19, the claim recites the limitation analogous to claim 4, and is rejected due to the same reason set forth above with respect to claim 4.
Regarding claim 22, Zhang teaches the limitation as follows:
A non-transitory computer-readable storage medium storing instructions which, when executed by a processor, cause the processor to perform a method of encoding a bitstream comprising: determining a feature value based on at least one of (i) neighboring reconstructed chroma samples of a current chroma block and (ii) neighboring reconstructed luma samples of a luma block that is collocated with the current chroma block (Figs. 11-13, ¶0044, 0139: video encoder 20 and video decoder 30 may be configured to calculate the Threshold as the average value of the neighboring coded (also be denoted as ‘reconstructed’) luma samples); grouping chroma samples of the current chroma block and luma samples of the luma block that is collocated with the current chroma block into a plurality of groups based on a threshold of the feature value, each of the plurality of groups including a respective chroma sample and a respective luma sample (Figs. 7A-7E, 11-13, ¶0129: Neighboring luma samples and neighboring chroma samples of the current block may be classified into several groups based on the values of the samples. ¶0135: T1−TM-1 are threshold levels for each classification group); determining a respective cross-component prediction mode from a plurality of candidate modes for each of the plurality of groups by comparing the respective chroma sample and the respective luma sample of each respective group to the determined feature value (¶0044, 0140: In one example, as illustrated in FIG. 7B, when M is equal to 3, neighboring samples may be classified into three groups. A neighboring sample (e.g., luma sample) with Rec′L[x,y]≦Threshold1 may classified into group 1; a neighboring sample with Threshold1<Rec′.sub.L[x,y]≦Threshold2 may be classified into group 2 and a neighboring sample with Rec′.sub.L[x,y]>Threshold2 may be classified into group 3. See eq (7), ¶0151-0155), each of the plurality of candidate modes corresponding to a distinct mathematical model (¶0253: video encoder 20 may be configured to classify the reconstructed luma samples that are greater than a first threshold as being in a first sample group of a plurality of sample groups, classify the reconstructed luma samples that are less than or equal to the first threshold as being in a second sample group of the plurality of sample groups…the second linear prediction model being different than the first linear prediction model); determining a respective set of model parameters for each of the plurality of groups, each of the sets of model parameters corresponding to a set of coefficients of the mathematical model of the respective cross-component prediction mode (See for example eq (7) group 1 equation having coefficient α1, group 2 equation having coefficient α2, group 3 equation having coefficient α3); encoding the current chroma block into the bitstream based on the determined cross- component prediction modes and the sets of model parameters of the plurality of groups ( ¶0253: video encoder 20 may be configured to determine parameters for each of the two or more linear prediction models using luma samples and chroma samples from blocks of video data that neighbor the first block of video data. In one example, video encoder 20 may be configured to classify the reconstructed luma samples that are greater than a first threshold as being in a first sample group of a plurality of sample groups, classify the reconstructed luma samples that are less than or equal to the first threshold as being in a second sample group of the plurality of sample groups. ¶0134: video encoder 20 may be configured to signal the number of classes in an encoded video bitstream to video decoder 30 in one or more of a PPS, SPS, and/or slice header); and transmitting the encoded bitstream (¶0090: the encoded bitstream may be transmitted to another device (e.g., video decoder 30) or archived for later transmission).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (US 20180077426 A1) in view of Wang et al. (US 20220360799 A1).
Regarding claim 8, Zhang teaches the method of claim 2, wherein the determining the feature value further comprises: determining the feature value as an average value of … the neighboring reconstructed luma samples of the collocated luma block (¶0268: Threshold may be calculated as the average value of the neighboring coded Luma samples).
Zhang does not explicitly teach determining the feature value as an average value of the neighboring reconstructed chroma samples of the current chroma block.
Wang teaches determining the feature value as an average value of the neighboring reconstructed chroma samples of the current chroma block (¶0150, 0156: computing the threshold chroma value includes finding an average chroma value from the plurality of reconstructed neighboring chroma samples).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Zhang by incorporating the teaching of Wang, in order to reduce the computational complexity in deriving the linear model parameters (Wang: ¶0120).
Claim(s) 13-14 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (US 20180077426 A1) in view of Kuo et al. (US 20250047886 A1).
Regarding claim 13, Zhang does not disclose wherein the determining the respective cross- component prediction mode for each of the plurality of groups further comprises: determining whether each of the plurality of groups shares a cross-component prediction mode based on a flag that is included in the coded information; and determining the cross-component prediction mode for each of the plurality of groups when the flag indicates that each of the plurality of groups shares the same cross-component prediction mode.
However, Kuo teaches wherein the determining the respective cross- component prediction mode for each of the plurality of groups further comprises: determining whether each of the plurality of groups shares a cross-component prediction mode based on a flag that is included in the coded information (¶0204, 0229: The proposed GLM can be combined with above discussed MMLM or ELM. When combined with classification, each group can share or have its own filter shape, with syntaxes indicating shape for each group. ¶0094: MMLM prediction mode, See Fig. 6 illustrating illustrates an example of classifying the neighboring samples into two groups based on the value Threshold); and determining the cross-component prediction mode for each of the plurality of groups when the flag indicates that each of the plurality of groups shares the same cross-component prediction mode (¶0204: The proposed GLM can be combined with above discussed MMLM or ELM. When combined with classification, each group can share or have its own filter shape, with syntaxes indicating shape for each group). Note that Kuo at para. ¶0204 discloses syntaxes indicating shape for each group. It is well-known in the art that syntaxes are used to convey information a decoder. In this case, the syntaxes are used to convey information whether each group can share or have its own filter shape.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Zhang by incorporating the teaching of Kuo as noted above, in order to improve the coding efficiency (Kuo:¶0181, 0210).
Regarding claim 14, Zhang teaches the method of claim 2, wherein the reconstructing the current chroma block further comprises: generating prediction samples for the chroma samples in each of the plurality of groups based on the respective cross-component prediction mode (¶0163: Video encoder 20 and video decoder 30 may be further configured to apply a first linear model (e.g., Model 1 of FIG. 10) to luma samples in the first classification group (represented by black circles in FIG. 13). Video encoder 20 and video decoder 30 may be further configured to apply a second linear model (e.g., Model 2 of FIG. 10) to luma samples in a second classification group (represented by white circles in FIG. 13). ¶0165: As illustrated in FIG. 13, video encoder 20 and video decoder 30 may be configured to apply Model 1 to coded luma samples … to derive the corresponding predicted chroma samples in the current block. Likewise, video encoder 20 and video decoder 30 may be configured to apply Model 2 to coded luma samples … to derive the corresponding predicted chroma samples in the current block).
Zhang does not explicitly disclose applying a filter on the prediction samples for the chroma samples in each of the plurality of groups.
However, Kuo discloses applying a filter on the prediction samples for the chroma samples in each of the plurality of groups (¶0202-0204: the gradient filter used for deriving the gradient direction can be the same or different with the GLM filter in shape… The proposed GLM can be combined with above discussed MMLM or ELM. When combined with classification, each group can share or have its own filter shape…).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Zhang by incorporating the teaching of Kuo, in order to improve the coding efficiency (Kuo:¶0181, 0210).
Regarding claim 20, Zhang does not explicitly disclose the method includes encoding a flag indicating that each of the plurality of groups shares the same cross-component prediction mode.
However, Kuo teaches the method includes encoding a flag indicating that each of the plurality of groups shares the same cross-component prediction mode (¶0204: The proposed GLM can be combined with above discussed MMLM or ELM. When combined with classification, each group can share or have its own filter shape, with syntaxes indicating shape for each group). Note that Kuo at ¶0204 discloses syntaxes indicating shape for each group. It is well-known in the art that syntaxes are used to convey information a decoder. In this case, the syntaxes are used to convey information whether each group can share or have its own filter shape.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Zhang by incorporating the teaching of Kuo as noted above, in order to improve the coding efficiency (Kuo: ¶0181, 0210).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NATHNAEL AYNALEM whose telephone number is (571)270-1482. The examiner can normally be reached M-F 9AM-5:30 PM ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, SATH PERUNGAVOOR can be reached at 571-272-7455. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/NATHNAEL AYNALEM/ Primary Examiner, Art Unit 2488
Prosecution Insights