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 .
Status of the Claims
Currently, claims 1-12 are pending in the application. Claims 1 & 11 are amended.
Continued Examination Under 37 CFR 1.114 1.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 05/12/2026 has been entered.
Response to Arguments / Amendments
Applicant’s arguments have been fully considered but are rendered moot in view of the new ground of rejection necessitated by amendments initiated by the applicant.
Claim Rejections - 35 USC § 103
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 of this title, 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.
Claims 1-12 are rejected under 35 U.S.C. 103 as being unpatentable over KATSAVOUNIDIS et al. (US 20190379895 A1, hereinafter KATSAVOUNIDIS) in view of Wei et al. (US 11297355 B1, hereinafter Wei) and Amirpour et al. (US 20230007298 A1, hereinafter Amirpour)
Regarding Claim 1, KATSAVOUNIDIS discloses a method for determining quality specifications for encoding representations of a video section, comprising:
determining a maximum quality level and a minimum quality level based on a quality measure ([0138], FIG. 7, quality threshold lists 710(0)-710(3) and a bitrate threshold list 720 such that quality threshold list 710(y) is associated with the quality metric 158(y) included in the quality metric list 156 that includes, without limitation, a minimum quality score 712(y) for the quality metric 158(y), a middle quality score 714(y) for the quality metric 158(y), and a maximum quality score 716(y) for the quality metric 158(y). The bitrate threshold list 720 includes, without limitation, a minimum bitrate 722, a middle bitrate 724, and a maximum bitrate 726), where the quality measure is based on a comparison with an unimpaired reference ([0025], iterative encoding application to perform shot-based encoding based on the shot sequences, the baseline configured encoder, a video multimethod assessment fusion (“VMAF”) metric, and a set of target VMAF scores; [0029]); and
determining a set of quality levels for the respective associated representations of a video section, wherein the set of quality levels comprises two or more quality levels ([0138], FIG. 7, quality threshold lists 710(0)-710(3) and a bitrate threshold list 720 such that quality threshold list 710(y) is associated with the quality metric 158(y) included in the quality metric list 156 that includes, without limitation, a minimum quality score 712(y) for the quality metric 158(y), a middle quality score 714(y) for the quality metric 158(y), and a maximum quality score 716(y) for the quality metric 158(y). The bitrate threshold list 720 includes, without limitation, a minimum bitrate 722, a middle bitrate 724, and a maximum bitrate 726), that maintain a predefined maximum quality gap between adjacent quality levels ([0146], baseline quality metric 158(0) is HVMAF, the quality metric 158(1) is LVMAF, the quality metric 158(2) is CPSNR, and the quality metric 158(3) is TPSNR. The low quality range for VMAF is bounded by the minimum quality score 712(0) of 30 and the middle quality score 714(0) of 63, the high quality range for VMAF is bounded by the middle quality score 714(0) of 63 and the maximum quality score 716(0) of 96 with the full quality range for VMAF is bounded by 0 and 100).
KATSAVOUNIDIS does not explicitly disclose where the set of quality levels
Wei teaches where the set of quality levels (Col. 15. ll. 31-45, (62), FIG. 4A, three different maximum bit rate settings: 1950 (represented by the circle marks), 5000 (represented by the triangle marks), and 9000 (represented by the plus marks) maximum kbps, as indicated by the legend 406. The circle marks in FIG. 4A depict the actual bit rates for the trial encoding outputs for all encoding profiles that include a value of 1950 for the maximum bit rate encoder parameter setting, as represented by the distribution of circle marks to the right of the 0 kbps and slightly to the left of the 2000 kbps horizontal axis ticks. These same circle marks also represent quality metric values (with higher values corresponding to higher quality for the VMAF quality metric), as represented by the range of values on the vertical axis between slightly above 75 to slightly below 94).
Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings set of quality levels comprises a quality level as taught by Wei (Col. 15. ll. 31-45) into the encoding & decoding system of KATSAVOUNIDIS in order to provide systems for streaming service to tailor video streams to a customer's playback device capability and bandwidth availability for dynamically adjusting live streaming video encoding profiles based on changing playback conditions for a client device (Wei, Col. 2, ll. 35-45).
Wei further teaches adjusting cmax to alter the area under the portion of the performance boundary curve 465 corresponding to higher bit rates, for the encoding profiles selected for the encoding ladder, the quality to bit rate tradeoff is weighted to favor greater quality for the 9000 kbps maximum bit rate, and a lower quality for the 1950 kbps maximum bit rate (Col. 19. ll. 53-59).
KATSAVOUNIDIS & Wei do not explicitly disclose the set of quality levels used in a bit ladder corresponds to selected representations having associated bit rates in the bit rate ladder.
Amirpour teaches the set of quality levels used in a bit ladder corresponds to selected representations having associated bit rates in the bit rate ladder ([0045],
FIGS. 5A-5D, a series of charts showing exemplary RD curves and convex hulls for an exemplary video sequence according to PSNR and VMAF and a set of three parameters may be used to optimize the bitrate ladder in PSTR, i.e., bitrate, resolution, and framerate. The bitrate-resolution-framerate triples that are as close as possible to the convex hull are selected for each bitrate;
In FIG. 6, a comparison is made between BD-Rates calculated according to: (i) conventional (i.e., “state-of-the-art”) methods wherein we optimize the bitrate ladder only over the spatial resolution—to form a convex hull, we select the bitrate-resolution-framerate triples using a framerate of 120 fps, and (ii) PSTR, wherein we optimize the bitrate ladder over both spatial and temporal resolutions, as described herein (e.g., FIGS. 4 and 7A-7B).
Table 1 in FIG. 6 summarizes bitrate savings (BD-Rate) against encoding at 1080p and 120 fps for a variety of example videos with varying video content. As shown in Table 1, for sequences with lower TI, the bitrate saving of PSTR over the state-of-the-art method is higher).
Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings set of the set of quality levels used in a bit ladder as taught by Amirpour ([0045]) into the encoding & decoding system of KATSAVOUNIDIS & Wei in order to provide systems for utilizing a multi-period per-scene optimization procedure that optimizes a bitrate ladder for maximum possible quality or minimum possible bitrate and allowing a video encoder to use spatial and temporal resolution downscaling to reduce the resolution of the video input, thus reducing the number of frames in the downscaled representation, and hence improving quality of experience (QoE) of the encoded video (Amirpour [0048]).
Regarding Claim 2, KATSAVOUNIDIS view of Wei and Amirpour discloses the method according to claim 1, KATSAVOUNIDIS discloses where the minimum quality level is determined based on an acceptance measure which indicates a minimum quality at which a predetermined number of viewers find the representation associated with the minimum quality to be acceptable ([0146], baseline quality metric 158(0) is HVMAF, the quality metric 158(1) is LVMAF, the quality metric 158(2) is CPSNR, and the quality metric 158(3) is TPSNR. The low quality range for VMAF is bounded by the minimum quality score 712(0) of 30 and the middle quality score 714(0) of 63, the high quality range for VMAF is bounded by the middle quality score 714(0) of 63 and the maximum quality score 716(0) of 96 with the full quality range for VMAF is bounded by 0 and 100).
Regarding Claim 3, KATSAVOUNIDIS view of Wei and Amirpour discloses the method according to claim 1, KATSAVOUNIDIS discloses where the maximum quality level is determined based on a quality at which a predetermined number of viewers cannot distinguish the representation corresponding to the quality from an original representation ([0146], baseline quality metric 158(0) is HVMAF, the quality metric 158(1) is LVMAF, the quality metric 158(2) is CPSNR, and the quality metric 158(3) is TPSNR. The low quality range for VMAF is bounded by the minimum quality score 712(0) of 30 and the middle quality score 714(0) of 63, the high quality range for VMAF is bounded by the middle quality score 714(0) of 63 and the maximum quality score 716(0) of 96 with the full quality range for VMAF is bounded by 0 and 100).
Regarding Claim 4, KATSAVOUNIDIS view of Wei and Amirpour discloses the method according to claim 1, KATSAVOUNIDIS discloses where a minimum number of quality levels is determined by the maximum quality level, the minimum quality level, and the maximum quality gap ([0026], iterative dynamic optimizer generates different encoded shot sequence based on shot-specific sets of encoding points and the associated configured encoder (either the baseline configured encoder or the candidate configured encoder). Each encoding point included in a shot-specific set of encoding points specifies a different combination of a resolution and a rate control parameter value).
Regarding Claim 5, KATSAVOUNIDIS view of Wei and Amirpour discloses the method according to claim 1, KATSAVOUNIDIS discloses where adjacent quality levels that differ by the maximum quality gap are classified as being subjectively equal by a predetermined number of viewers ([0026], each video encode point specifies a different encoded video sequence, the VMAF score of the encoded video sequence, and the bitrate of the encoded video sequence. Each encoded video sequence includes a set of encoded shot sequences that span the length of the source video sequence. Notably, the video encode points in the global convex hull, for the source video sequence and the encoding points, minimize the bitrate for different VMAF scores. Accordingly, the global convex hull can be used to define an optimized bitrate-quality curve).
Regarding Claim 6, KATSAVOUNIDIS view of Wei and Amirpour and Amirpour discloses the method according to claim 1, KATSAVOUNIDIS discloses where the quality measure is an estimate of a subjective video metric ([0054], valid HVMAF scores range from 0 to 100, where the estimated human-perceived visual quality increases as the HVMAF score increases)
Regarding Claim 7, KATSAVOUNIDIS view of Wei and Amirpour discloses the method according to claim 1, KATSAVOUNIDIS discloses where the quality measure is a Video Multi-Method Assessment Fusion (VMAF) metric ([0054], FIG. 1, baseline quality metric 158(0) is HVMAF, each of the target quality scores 154 is a different HVMAF score).
Regarding Claim 8, KATSAVOUNIDIS view of Wei and Amirpour discloses the method according to claim 7, KATSAVOUNIDIS discloses where the maximum quality gap in the VMAF metric is 2; and/or the maximum quality level in the VMAF metric is 95; and/or the minimum quality level in the VMAF metric is 55 ([0054], FIG. 1 depicts exemplary values for each of the target quality scores 154. Because the baseline quality metric 158(0) is HVMAF, each of the target quality scores 154 is a different HVMAF score. As a general matter, valid HVMAF scores range from 0 to 100, where the estimated human-perceived visual quality increases as the HVMAF score increases. As shown, the target quality scores 154(0)-154(11) are, respectively, 36, 42, 48, 54, 60, 66, 72, 78, 84, 90, 96. The target quality scores 154 are spaced at intervals of 6 to reflect empirical results indicating that a just-noticeable-difference in human-perceived visual quality corresponds to a VMAF difference of 6. Further, the target quality score 154(0) of 36 reflects an empirically determined lowest acceptable visual quality and the target quality score 154(11) of 96 reflects an empirically determined visual quality that is close to perceptually perfect visual quality. In alternate embodiments, the target quality scores 154 may be determined in any technically feasible fashion).
Regarding Claim 9, KATSAVOUNIDIS view of Wei and Amirpour discloses the method according to claim 1, KATSAVOUNIDIS discloses further comprising: determining one or more encoding parameters for each quality level in the set such that the representation of a video section, after encoding with one or more encoding parameters, substantially reaches the quality level associated with the representation of the video section ([0141] In general, to map a given quality score 348 (e.g., the minimum quality score 712(0)) to a bitrate 246 via a given global convex hull 390, the comparison engine 180 determines the bitrate 246 specified by the bitrate-quality curve 630 associated with the global convex hull 390 for the quality score 348. Conversely, to map a given bitrate 246 to a quality score 348 via a given global convex hull 390, the comparison engine 180 determines the quality score 348 specified by the bitrate-quality curve 630 associated with the global convex hull 390 for the bitrate 246).
Regarding Claim 10, Computer-readable claim 10 of using the corresponding method claimed in claims 1, and the rejections of which are incorporated herein for the same reasons as used above.
Regarding Claim 11, Device claim 11 of using the corresponding method claimed in claims 1, and the rejections of which are incorporated herein for the same reasons as used above.
Regarding Claim 12, A device for encoding claim 12 of using the corresponding method claimed in claims 9, and the rejections of which are incorporated herein for the same reasons as used above.
Conclusion
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/SAMUEL D FEREJA/Primary Examiner, Art Unit 2487
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